Digital watermark inserting system and digital watermark characteristic parameter table generating method

ABSTRACT

A digital watermark inserting system for inserting digital watermark information into an input image is disclosed, that comprises a categorizing portion  103  for calculating a feature amount of the input image, categorizing the input image to a category index, a digital watermark characteristic calculating portion  104  for calculating an image deteriorating ratio and a robustness evaluation value corresponding to a digital watermark strength based on a robustness evaluation value calculation parameter and the category index, a digital watermark strength calculating portion  100  for outputting the digital watermark strength to the portion  104 , deciding the optimum digital watermark strength based on digital watermark strength restriction information, and outputting the optimum digital watermark strength, and a digital watermark inserting portion  102  for converting input embedding data into digital watermark information, inserting the digital watermark information into the input image.

CROSS REFERENCE TO RELATED APPLICATION

The present application is a divisional of copending application Ser.No. 09/480,023 filed on Jan. 10, 2000.

BACKGROUND OF THE INVENTION

1. Field of the Invention

The present invention relates toga digital watermark inserting systemfor inserting digital watermark information into an input image, adigital watermark characteristic parameter table generating method, anda computer readable record medium on which a digital watermarkcharacteristic parameter table generating program has been recorded.

2. Description of the Related Art

In recent years, digital watermarks are becoming attractive. However,there have been few studies that focus on attacks against digitalwatermarks. As a result, it is difficult to compare robustness valuesagainst attacks in different digital watermark systems. As a related artreference, a framework of a robustness evaluation value calculatingsystem for categorizing attacks that take place in conventional imageprocessing and so forth and calculating robustness evaluation values forindividually categorized attacks has been disclosed by Ryoma Oami,Yoshihiro Miyamoto, and Mutsumi Ohta, NEC C & C Media ResearchLaboratories in “Robustness Measure against attacks for digitalwatermarking and its application,” 1998 Image Media Processing Symposium(IMPS 98). The related art reference describes a method for obtainingthe optimum digital watermark strength.

In the related art reference, attacks against digital watermarks arelargely categorized as (1) deterioration that takes place in an imageprocessing or the like and (2) intentional forgery of embeddedinformation. Since attacks of the category (1) inevitably take place ina conventional image process. Thus, strong robustness is required forattacks of the category (1). Attacks of the category (2) are higherlevel attacks than those of the category (1). The attacks of thecategory (2) largely depend on the digital watermark inserting anddetecting system. In the related art reference, attacks of the category(1) are further categorized from a view point of a signal processing.Robustness evaluation values are calculated for individually categorizedattacks. The categorized attacks are for example coding loss in JPEG(LC), uniform and Gauss type noise (N), geometric transform such asscale and rotation (GT), pixel value conversion such as gray and binary(PVC), and image processing such as sharpening and median filtering(IP).

In the system of the related art reference, a digital watermark isinserted into an image. Thus, a digital watermark inserted image isobtained. Thereafter, the digital watermark inserted image is attackedin a predetermined manner and then the digital watermark is detected. Byrepeating the above procedures, varying a parameter for adjusting theattack strength, a detection ratio is obtained. The detection ratio datais statistically processed. Thus, robustness evaluation values forindividually categorized attacks are calculated. As the statisticalprocess, weighted means method or threshold value method is used, forexample. In the evaluation value calculating system, (a) by properlysetting a weighting function, evaluation values for applications havingdifferent attack characteristics can be calculated; (b) robustnessevaluation values can be compared among different systems regardless ofthe values of the attack strength x for really measuring detectionratios; and (c) the accuracy of evaluation values can be improvedprogressively. According to the evaluation value calculating system,evaluation values 0 to 1 against watermark strength values 1 to 4 dependon conventional digital watermark systems.

However, according to the digital watermark inserting system of therelated art reference, it is difficult to properly set the strength of adigital watermark. The strength of the digital watermark largely dependson the contents of the image. Even if the user designates the relationof the strength of digital watermarks, the deterioration of the imagequality, and the robustness values against attacks in a deterministicmanner, the relation cannot be applied to all images. Thus, the optimumstrength of digital watermarks cannot be obtained.

The inventor of the present invention has invented “digital watermarkinserting system” that is currently being filed as Japanese PatentApplication No. 10-150823 (hereinafter, this invention may be referredto as second related art reference). This invention was made to embed(insert) copyright information and so forth into digital signals ofaudio data, image data, and so forth.

FIG. 1 is a block diagram showing the structure of the “digitalwatermark inserting system” as the second related art reference. In FIG.1, a categorizing portion 103 calculates a feature amount of an inputimage, obtains a category of the image with the calculated featureamount, and outputs the index indicating the category as a categoryindex to a storing unit 2001. The storing unit 2001 selects a tablecorresponding to the category index that is received from thecategorizing portion 103 and outputs an image quality deteriorationratio and a robustness evaluation value corresponding to digitalwatermark strength that is received from a digital watermark strengthcalculating portion 100 to the digital watermark strength calculatingportion 100.

The digital watermark strength calculating portion 100 outputs variousdigital watermark strength values to the storing unit 2001. The digitalwatermark strength calculating portion 100 decides the optimum digitalwatermark strength based on the image quality deterioration ratio andthe robustness evaluation value that are received from the storing unit2001 and based on restriction information of digital watermark strengththat is input by the user and outputs the decided optimum digitalwatermark strength data to a digital watermark inserting portion 102.

The digital watermark inserting portion 102 converts embedding data intodigital watermark information, inserts the digital watermark into theimage with the optimum digital watermark strength received from thedigital watermark strength calculating portion 100, and outputs adigital watermark inserted image.

Next, the operation of the digital watermark inserting system shown inFIG. 1 will be described. First of all, several symbols used in theoperation will be defined.

The number of categories of input images is denoted by K. K categoriesare distinguished with category index k (where k=1, . . . , K). Thedigital watermark strength with which a digital watermark is inserted isdenoted by s(m) (where m=1, . . . , M). The parameter used as thedigital watermark strength depends on the digital watermark insertingalgorithm for use. When the digital watermark strength is successivelyvaried, it is digitized into M different values and their values aredenoted by s(m). When the category index is k and the digital watermarkstrength is s(m), the image quality deterioration ratio and therobustness evaluation value against the attack are denoted by D(k,m) andV(k,m), respectively.

Next, with reference to FIG. 1, the operation of the digital watermarkinserting system will be described. An input image is supplied to thecategorizing portion 103. The categorizing portion 103 calculates afeature amount of the image, decides the category of the input imagebased on the obtained feature amount, and outputs a category index thatrepresents the category. In reality, the categorizing portion 103 storesfeature amount values that represent boundaries of categories. Thecategorizing portion 103 compares the stored feature amount values withthe calculated feature amount value and categorizes the input imagebased on the compared result. The feature amount is for example anactivity of the entire image (the activity is the mean value of ACfrequency components).

The category index that is output from the categorizing portion 103 isinput to the storing unit 2001. The storing unit 2001 stores digitalwatermark feature tables for individual category indexes. Each of thedigital watermark feature tables represents the relation of the digitalwatermark strength values, the image quality deterioration ratios andthe robustness evaluation values against attacks. A digital watermarkcharacteristic table for a category index k is shown in Table 1.

TABLE 1 Image quality Robustness Digital watermark deteriorationevaluation values strength amount against attack s(1) D(k, 1) V(k, 1)s(2) D(k, 2) V(k, 2) . . . . . . . . . s(M) D(k, M) V(k, M)

In addition, when a digital watermark strength s(m) is input from thedigital watermark strength calculating portion 100, the storing unit2001 selects a digital watermark characteristic table corresponding tothe category index k that is received from the categorizing portion 103,and outputs the image quality deterioration amount D(k, m) and therobustness evaluation value V(k, m) to the digital watermark strengthcalculating portion 100.

After the input image has been supplied to the system and thecategorizing portion 103 has calculated the category index k, thedigital watermark strength calculating portion 100 calculates theoptimum digital watermark strength based on digital watermark strengthrestriction information that is input by the user. Basically, thedigital watermark strength that maximizes the following objectivefunction is defined as the optimum digital watermark strength.

Z(m)=(1−a)(1−D(k, m))+aV(k, m).  (1)

where a satisfies the relation of 0≦a≦1. Z(m) is calculated for eachdigital watermark strength value. The digital watermark strength s(m)with the maximum value is calculated as the optimum digital watermarkstrength.

The optimum digital watermark strength that is output from the digitalwatermark strength calculating portion 100 is input to the digitalwatermark inserting portion 102. The digital watermark inserting portion102 converts the input embedding data into digital watermark informationand inserts the input embedding data into the image. The digitalwatermark strength used in inserting the input embedding data into theimage is the optimum digital watermark strength that is output from thedigital watermark strength calculating portion 100. The resultant imageis output as a digital watermark inserted image.

According to the second related art reference, the digital watermarkinserting algorithm is not limited as long as the user can designate thedigital watermark strength or the like for the digital watermarkinserted into the image. For example, the watermarking algorithmdisclosed in Japanese Patent Laid-Open Publication No. 9-191394 andperiodical “IEEE Transactions on Image Processing,” Vol. IP-6, pp.1673-1687, No. 12, 1997 can be used.

In this algorithm, the entire image is processed by discrete cosinetransform (DCT) method or discrete Fourier transform (DFT) method. The Nlargest transform coefficients are selected from the obtained transformcoefficients. Thereafter, digital watermark information is inserted. Inreality, digital watermark information is inserted corresponding to thefollowing formula.

ν′=ν+αx  (2)

or

ν′=ν(1+αx)  (3)

where x is a digital watermark signal; ν is a transform coefficient intowhich the watermark signal is embedded; α is the digital watermarkstrength; and ν′ is a digital watermark inserted transform coefficient.For the obtained digital watermark inserted transform coefficient,inverse DCT method or inverse DFT is performed. Thus, a digitalwatermark inserted image is generated and output. In this algorithm, thedigital watermark strength is represented by parameter α in the formula(2) or the formula (3).

Next, a digital watermark characteristic table generating unit thatgenerates a digital watermark characteristic table stored in the storingunit 2001 of the system shown in FIG. 1 will be described. FIG. 2 is ablock diagram showing the structure of a conventional digital watermarkcharacteristic table generating unit. In FIG. 2, a digital watermarkinserting portion 200 converts embedding data into proper data, insertsdigital watermark information with the input digital watermark strengthinto the input image, and outputs the resultant digital watermarkinserted image to an attack executing portion 201. The attack executingportion 201 attacks the digital watermark inserted image with apredetermined strength corresponding to an input attack parameter in apredetermined manner and outputs the attacked image to a digitalwatermark detecting portion 202.

The digital watermark detecting portion 202 detects a digital watermarkfrom the attacked image that is received from the attack executingportion 201 and outputs the detected result to a digital watermarkcharacteristic table generating portion 2201. An image qualitydeterioration amount calculating portion 203 calculates an image qualitydeterioration amount with both the digital watermark inserted image thatis received from the digital watermark inserting portion 200 and theinput image and outputs the calculated image quality deteriorationamount to the digital watermark characteristic table generating portion2201. A categorizing portion 204 categorizes the input image and outputsa category index corresponding to the categorized result to the digitalwatermark characteristic table generating portion 2201.

The digital watermark characteristic table generating portion 2201obtains a robustness evaluation value against the attack and an imagequality deterioration ratio, based on the detected result that isreceived from the digital watermark detecting portion 202, the digitalwatermark strength, the attack parameter, the image qualitydeterioration amount that is received from the image qualitydeterioration amount calculating portion 203, and the category indexthat is received from the categorizing portion 204, and it outputs therelation of the robustness evaluation value, the image qualitydeterioration ratio, and the digital watermark strength as a digitalwatermark characteristic table.

Next, the operation of the digital watermark characteristic tablegenerating unit shown in FIG. 2 will be described. For easyunderstanding, several symbols necessary for explaining the operation ofthe digital watermark characteristic table generating unit will bedefined.

The number of input images is denoted by I. The I input images aredistinguished by an index i (where i=1, . . . , I). The value of theattack parameter is denoted by x(j) (where j=1, . . . , J). The attackparameter is a parameter for adjusting the attack strength. The categoryindex k (where k=1, . . . , K), the digital watermark strength s(m)(where m=1, . . . , M), the image quality deterioration ratio D(k, m),and the attack robustness evaluation value V(k, m) are defined asdescribed above. A category index for an input image i is denoted byk(i). An image quality deterioration amount for an input image i isdenoted by d(i). When the category index is denoted by k, the digitalwatermark strength index is denoted by m, and the attack parameter indexis denoted by j, the detected result and the detection ratio are denotedby y(k, m, j) and r(k, m, j), respectively.

Next, with reference to FIG. 2, the operation of the digital watermarkcharacteristic table generating unit will be described. An input image iis supplied to the digital watermark inserting portion 200. Inputembedding data is converted into digital watermark information. With aparameter of input digital watermark strength s(m), the digitalwatermark is inserted into the image. The obtained image is output as adigital watermark inserted image to the image quality deteriorationamount calculating portion 203 and the attack executing portion 201.

The attack executing portion 201 attacks the digital watermark insertedimage in a predetermined manner and outputs the attacked image to thedigital watermark detecting portion 202. The attack strength is adjustedby the input attack parameter x(j). When the digital watermark insertedimage is attacked by a noise adding attack, the attack parameter is anamount of noise power, noise amplitude, PSNR (Peak Signal to NoiseRatio), or the like. When the digital watermark inserted image isattacked as an enlarging attack or a shrinking attack, the attackparameter is an amount of enlargement/shrinkage magnification orequivalent amount.

The attacked image that is output from the attack executing portion 201is input to the digital watermark detecting portion 202. The digitalwatermark detecting portion 202 detects a digital watermark from theattacked image. When the digital watermark detecting portion 202 hasdetected an embedded digital watermark, it outputs “1” as a detectedresult. When the digital watermark detecting portion 202 has notdetected an embedded digital watermark, it outputs “0” as a detectedresult. When the digital watermark detecting portion 202 has detectedpart of an embedded digital watermark, it outputs a value between “0”and “1” (for example, “0.5”) as a detected result. The data that isoutput from the digital watermark detecting portion 202 is input to thedigital watermark characteristic table generating portion 2201.

Both the input image and the digital watermark inserted image that isoutput from the digital watermark inserting portion 200 are input to theimage quality deterioration amount calculating portion 203. The imagequality deterioration amount calculating portion 203 compares the inputimage with the digital watermark inserted image and calculates the imagequality deterioration amount due to the inserted digital watermark. Asthe image quality deterioration amount, a PSNR value of the digitalwatermark inserted image against the original image or a WSNR (WeightedSignal to Noise Ratio) value in consideration of visual characteristicsis used. Alternatively, a ratio of the deterioration to a JND (JustNoticeable Distortion), which is derived by dividing the differencesbetween the digital watermark inserted image and the original inputimage by JND values after the JND values are calculated, can be used.The calculated image quality deterioration amount is output to thedigital watermark characteristic table generating portion 2201.

In FIG. 2, the input image is also supplied to the categorizing portion204. The operation of the categorizing portion 204 is the same as thatof the categorizing portion 103 shown in FIG. 1. The categorizingportion 204 calculates a feature amount of the input image andcategorizes the input image based on the calculated feature amount.Thereafter, the categorizing portion 204 outputs a category index thatrepresents the category to the digital watermark characteristic tablegenerating portion 2201.

The digital watermark characteristic table generating unit shown in FIG.2 performs such a process for I input images i=1, . . . , I. For eachinput image i, the procedures described above are performed with the Mdifferent digital watermark strength values s(m) (where m=1, . . . , M).For each digital watermark strength s(m), the procedures described aboveare performed with the J different attack parameters x(j) (where j=1, .. . , J). The detected results y(k(i), m, j), the digital watermarkstrength s(m), the index m, the attack parameter x(j), the index J, theimage quality deterioration amount d(k(i), m), and the category indexk(i) are supplied to the digital watermark characteristic tablegenerating portion 2201. The digital watermark characteristic tablegenerating portion 2201 generates and outputs a digital watermarkcharacteristic table describing the relation between these inputfactors.

Next, the digital watermark characteristic table generating portion2201(2303) will be described. FIG. 3 is a block diagram showing thestructure of the digital watermark characteristic table generatingportion 2201(2303). A detected result totaling portion 300 totals thedetected result of the digital watermark detecting portion 202 for eachattack parameter, each digital watermark strength, and each categoryindex, calculates a detection ratio with the totaled result, and outputsthe calculated detection ratio to a robustness evaluation valuecalculating portion 2301.

An image quality deterioration amount totaling portion 301 totals theimage quality deterioration amount that is received from the digitalwatermark deterioration amount calculating portion 203 for each categoryindex and each digital watermark strength, calculates an image qualitydeterioration ratio with the totaled image quality deterioration amount,and outputs the calculated image quality deterioration ratio to a datacombining portion 2302.

Next, a robustness evaluation value calculating portion 2301 calculatesan attack robustness evaluation value with the attack parameter and thedetection ratio that is received from the detected result totalingportion 300 and outputs the calculated attack robustness evaluationvalue to the data combining portion 2302. For each category index, thedata combining portion 2302 generates a table that describes therelation between the digital watermark strength, the image qualitydeterioration ratio, and the robustness evaluation value, and outputsthe table as a digital watermark characteristic table.

Next, the operation of the digital watermark characteristic tablegenerating portion shown in FIG. 3 will be described. The digitalwatermark detected result y(k(i), m, j) is input to the detected resulttotaling portion 300. The detected result totaling portion 300 has astoring means. The detected result totaling portion 300 totals thedigital watermark detected result y(k(i), m, j) for each category indexk, each digital watermark strength index m, and each attack parameterindex j and calculates a mean value r(k, m, j) as a detection ratio, andoutputs the detection ratio r(k, m, j) to the robustness evaluationvalue calculating portion 2301.

The robustness evaluation value calculating portion 2301 calculates anattack robustness evaluation value V(k, m) based on the detection ratior(k, m, j) that is received from the detected result totaling portion300 and outputs the attack robustness evaluation value V(k, m) to thedata combining portion 2303. The operation of the robustness evaluationvalue calculating portion 2301 will be described later.

On the other hand, the image quality deterioration amount d(i) is inputto the image quality deterioration amount totaling portion 301. Theimage quality deterioration amount totaling portion 301 has a storingmeans. The image quality deterioration amount totaling portion 301totals the image quality deterioration amount d(i) for each categoryindex k and each digital watermark strength index m, calculates the meanvalue D(k, m) as an image quality deterioration ratio, and outputs thecalculated mean value D(k, m) to the data combining portion 2302.

The data combining portion 2302 combines the robustness evaluation valueV(k, m) that is received from the robustness evaluation valuecalculating portion 2301, the image quality deterioration ratio D(k, m)that is received from the image quality deterioration amount totalingportion 301, and the digital watermark strength s(m) and generates andoutputs a digital watermark characteristic table shown in FIG. 1 foreach category index k. Next, the robustness evaluation value calculatingmethod performed by the robustness evaluation value calculating portion2301 shown in FIG. 3 will be described.

To calculate the robustness evaluation value, the variation of thedetection ratio in the case that the attack parameter x is successivelyvaried is considered. The robustness evaluation value V(k, m) is givenby the following formula. $\begin{matrix}{{V\left( {k,\quad m} \right)} = {\int_{- \infty}^{\infty}{{w(x)}{r\left( {k,\quad m,\quad x} \right)}\quad {x}}}} & (4)\end{matrix}$

where k is a category index; m is a digital watermark strength index; xis an attack parameter; r(k, m, x) is a detection ratio that is afunction of the attack parameter x when the digital watermark strengthindex is m; and W(X) is a weighting function.

The weighting function W(X) determines the degree of contribution of thedetection ratio at each attack parameter x to the robustness evaluationvalue V(k, m). When the weighting function is properly set, the user'ssense against the deterioration due to an attack and attack frequencyinformation can be affected to the evaluation value. In reality, thedetection ratio against the digitized value x(j) is obtained rather thanthe detecting ratio against any attack parameter x. Thus, the robustnessevaluation value V(k, m) is calculated by digitizing and approximatingthe formula (4).

Alternatively, the robustness evaluation value V(k, m) can be obtainedcorresponding to the following formula: $\begin{matrix}{{V\left( {k,m} \right)} = {\frac{1}{L}{\int_{- \infty}^{\infty}{{T\left( {{r\left( {k,m,x} \right)},\alpha} \right)}{x}}}}} & (5)\end{matrix}$

where k is a category index; m is a digital watermark strength index;V(k, m) is a robustness evaluation value V(k, m); and L is a referenceinterval length of the attack parameter. T(x, α) is a thresholdingfunction given by the following formula: $\begin{matrix}{{T\left( {x,a} \right)} = \left\{ \begin{matrix}1 & \left( {x > \alpha} \right) \\0 & \left( {x \leq \alpha} \right)\end{matrix} \right.} & (6)\end{matrix}$

However, the second related art reference has the following problems.

As a first problem, it is difficult for the user to customize an attackrobustness evaluation value calculating method. Although it is preferredfor the user to freely designate a weighting function and a thresholdvalue for calculating a robustness evaluation value, the digitalwatermark inserting system of the second related art reference uses apre-calculated robustness evaluation value as an attack robustnessevaluation value, it is difficult to tune the robustness evaluationvalue calculating method.

As a second problem, when a robustness evaluation value for acombination of a plurality of attacks is used for calculating theoptimum digital watermark strength, the data amount to be storedadversely increases. Thus, it is necessary to reduce the data amount. Inother words, according to the second related art reference, since allrobustness evaluation values corresponding to combinations of aplurality of attacks should be stored, a huge amount of storage capacityis required. However, it is difficult to satisfy such a huge storagecapacity.

SUMMARY OF THE INVENTION

An object of the present invention is to provide a digital watermarkinserting system that allows the optimum digital watermark strength tobe automatically calculated corresponding to a robustness evaluationvalue against an attack and an image quality deterioration ratio.

A first aspect of the present invention is a digital watermark insertingsystem for inserting digital watermark information into an input image,comprising a means for calculating a feature amount of the input image,categorizing the input image, and outputting a category index as thecategorized result, a digital watermark characteristic calculating meansfor calculating an image deteriorating ratio and a robustness evaluationvalue against a digital watermark strength based on a robustnessevaluation value calculation parameter and the category index, therobustness evaluation value calculation parameter being input by theuser, a digital watermark strength calculating means for outputting thedigital watermark strength to the digital watermark characteristiccalculating means, deciding the optimum digital watermark strength basedon digital watermark strength restriction information that is input bythe user, and outputting the optimum digital watermark strength, and adigital watermark inserting means for converting input embedding datainto digital watermark information, inserting the digital watermarkinformation into the input image with an input parameter of the optimumdigital watermark strength, and outputting the resultant image as adigital watermark inserted image.

A second aspect of the present invention is a digital watermarkcharacteristic parameter table generating method for inserting digitalwatermark information into an input image, comprising the steps ofcalculating a feature amount of the input image, categorizing the inputimage with the calculated result, and outputting a category index as thecategorized result, converting input embedding information into thedigital watermark information, inserting the digital watermarkinformation into the input image with input digital watermark strength,and generating a digital watermark inserted image as the inserted data,adjusting the strength of an attack with an input attack parameter,attacking the digital watermark inserted image with the adjusted attackstrength, generating a resultant attacked image, detecting a digitalwatermark from the attacked image, outputting the detected result,comparing the input image with the digital watermark inserted image,calculating an image quality deterioration amount caused by the inserteddigital watermark, and outputting the calculated the image qualitydeterioration ratio amount, and receiving the detected result of thedigital watermark, the digital watermark strength, the attack parameter,the image quality deterioration amount, and the category index, totalingthe detected results for each of combinations of the category index, thedigital watermark strength, and the attack parameter, obtaining adetection ratio as the totaled result, totaling the image qualitydeterioration amount for each of combinations of the category index andthe digital watermark strength, obtaining an image quality deteriorationratio as the totaled result, and calculating a digital watermarkcharacteristic parameter table by using the detection ratio and theimage quality deterioration ratio, and outputting the digital watermarkcharacteristic parameter table.

A third aspect of the present invention is a record medium from which acomputer reads a program that causes the computer to drive a digitalwatermark inserting system for inserting digital watermark informationinto an input image, the system comprising a means for calculating afeature amount of the input image, categorizing the input image, andoutputting a category index as the categorized result, a digitalwatermark characteristic calculating means for calculating an imagedeteriorating ratio and a robustness evaluation value corresponding to adigital watermark strength based on a robustness evaluation valuecalculation parameter and the category index, the robustness evaluationvalue calculation parameter being input by the user, a digital watermarkstrength calculating means for outputting the digital watermark strengthto the digital watermark characteristic calculating means deciding theoptimum digital watermark strength based on digital watermark strengthrestriction information that is input by the user, and outputting theoptimum digital watermark strength, and a digital watermark insertingmeans for converting input embedding data into digital watermarkinformation, inserting the digital watermark information into the inputimage with an input parameter of the optimum digital watermark strength,and outputting the resultant image as a digital watermark insertedimage.

A fourth aspect of the present invention is a record medium from which acomputer reads a program that causes the computer to perform a methodfor inserting digital watermark information into an input image, themethod comprising the steps of (a) calculating a feature amount of theinput image, categorizing the input image, and outputting a categoryindex as the categorized result, (b) digital watermark characteristiccalculating means for calculating an image deteriorating ratio and arobustness evaluation value corresponding to a digital watermarkstrength based on a robustness evaluation value calculation parameterand the category index, the robustness evaluation value calculationparameter being input by the user, (c) digital watermark strengthcalculating means for outputting the digital watermark strength to step(b), deciding the optimum digital watermark strength based on digitalwatermark strength restriction information that is input by the user,and outputting the optimum digital watermark strength, and (d) digitalwatermark inserting means for converting input embedding data intodigital watermark information, inserting the digital watermarkinformation into the input image with an input parameter of the optimumdigital watermark strength, and outputting the resultant image as adigital watermark inserted image.

Next, with reference to the accompanying drawings, the present inventionwill be described. A digital watermark inserting system according to thepresent invention comprises a means (103, FIG. 4) for calculating afeature amount of the input image, categorizing the input image andoutputting a category index as the categorized result, a digitalwatermark characteristic calculating means (104, FIG. 4) for calculatingan image deteriorating ratio and a robustness evaluation valuecorresponding to a digital watermark strength based on a robustnessevaluation value calculation parameter and the category index, therobustness evaluation value calculation parameter being input by theuser, a digital watermark strength calculating means (100, FIG. 4) foroutputting the digital watermark strength to the digital watermarkcharacteristic calculating means (104, FIG. 4), deciding the optimumdigital watermark strength based on digital watermark strengthrestriction information that is input by the user, and outputting theoptimum digital watermark strength, and a digital watermark insertingmeans (102, FIG. 4) for converting input embedding data into digitalwatermark information, inserting the digital watermark information intothe input image with an input parameter of the optimum digital watermarkstrength, and outputting the resultant image as a digital watermarkinserted image.

In the digital watermark inserting system according to the presentinvention, the digital watermark characteristic calculating means (104.FIG. 4) has a first storing means (101, FIG. 5) for storing a digitalwatermark characteristic parameter table for each of various categoryindexes, the digital watermark characteristic parameter table describingthe relation of a digital watermark strength, an image detection ratio,and a detection ratio parameter, the detection ratio parameterdescribing a detection ratio curve/curved surface that approximates thevariation of the detection ratio of the digital watermark informationagainst an attack parameter, selecting a detection ratio characteristicparameter table corresponding to the category index, and outputting theimage quality deterioration ratio and the detection ratio characteristicparameter corresponding to the digital watermark strength that is outputfrom the digital watermark strength calculating means (100, FIG. 4), anda robustness evaluation value calculating means (105, FIG. 5) forobtaining the detection ratio curve/curved surface with the detectionratio characteristic parameter, performing a statistic process based onthe robustness evaluation value calculation parameter that is input bythe user, calculating the robustness evaluation value, and outputtingthe robustness evaluation value. In the digital watermark insertingsystem according to the present invention, the digital watermarkcharacteristic calculating means (104, FIG. 4) has a second storingmeans (171, FIG. 6) for storing a digital watermark characteristicparameter table describing the relation of a category index, an imagequality deterioration ratio curve parameter describing an image qualitydeterioration ratio curve that approximates the variation of the imagequality deterioration ratio against a digital watermark strength, and adetection ratio characteristic general parameter that describes adetection ratio characteristic parameter curve approximating thevariation of a detection ratio characteristic parameter against thedigital watermark strength and outputting an image quality deteriorationratio curve parameter and a detection ratio characteristic generalparameter corresponding to the category index, an image qualitydeterioration ratio calculating means (100, FIG. 4) for obtaining animage quality deterioration ratio curve with the image qualitydeterioration ratio curve parameter, calculating the image qualitydeterioration ratio corresponding to the digital watermark strength thatis output from the digital watermark strength calculating portion, andoutputting the calculated image quality deterioration ratio, and arobustness evaluation value calculating means (173, FIG. 6) forobtaining the detection ratio characteristic parameter curve with thedetection ratio characteristic general parameter, calculating adetection ratio characteristic parameter corresponding to the digitalwatermark strength that is output from the digital watermark strengthcalculating portion, obtaining the detection ratio curve/curved surfacewith the calculated detection ratio characteristic parameter, performinga statistic process based on the robustness evaluation value calculationparameter that is input by the user, and outputting the calculatedrobustness evaluation value as the processed result.

In the digital watermark inserting system according to the presentinvention, the robustness evaluation value calculating means (105, FIG.5 or 173, FIG. 6) obtains an inner product of the detection ratio curveand a weighting function so as to calculate the robustness evaluationvalue.

In the digital watermark inserting system according to the presentinvention, the robustness evaluation value calculating means (105, FIG.5 or 173, FIG. 6) obtains a region of an attack parameter of which adetection ratio exceeds a predetermined threshold value with thedetection ratio curve and calculates the robustness evaluation valuebased on the length of the region.

In the digital watermark inserting system according to the presentinvention, the detection ratio characteristic parameter is a detectionratio curve parameter that represents the detection ratio curve for asingle attack.

In the digital watermark inserting system according to the presentinvention, the detection ratio characteristic parameter is composed ofthe detection ration curve parameter for a single attack and an attackcorrelation curved surface parameter that is a parameter that describesan attack correlation curved surface approximating an attack correlationvalue defined based on the ratio of the product of detection ratios ofsingle attacks and a detection ratio for a complex attack, and therobustness evaluation value calculating means (105, FIG. 5) obtains thedetection ratio curve for a single attack composing a complex attackwith the detection ratio curve parameter for the single attack, obtainsan attack correlation curved surface with the attack correlation curvedsurface parameter, obtains the detection ratio curved surface for thecomplex attack based on the product of the detection ratio curve for thesingle attack and the attack correlation curved surface, and calculatesthe robustness evaluation value.

In the digital watermark inserting system according to the presentinvention, the detection ratio general parameter is a detection ratiocurve general parameter that represents a curve approximating thevariation of the detection ratio curve parameter against a digitalwatermark strength for a single attack. In the digital watermarkinserting system according to the present invention, the detection ratiocharacteristic general parameter is composed of a detection ratio curvegeneral parameter for a single attack and an attack correlation curvedsurface general parameter that represents a curve approximating thevariation of an attack correlation curve parameter against the digitalwatermark strength. The robustness evaluation value calculating means(173, FIG. 6) obtains the detection ratio curve for a single attackcomposing a complex attack with a detection ratio curve generalparameter for the single attack, obtains an attack correlation curvedsurface with the attack correlation curved surface general parameter,obtains the detection ratio curved surface for the complex attack basedon the product of the detection ratio curve for the single attacks andthe attack correlation curved surface, and calculates the robustnessevaluation value.

In the digital watermark inserting system according to the presentinvention, the attack correlation curved surface and the weightingfunction for a complex attack are a linear sum of a function separablefor an attack parameter of each attack.

In the digital watermark inserting system according to the presentinvention, the restriction information of the digital watermark strengthis an allowable limit value of the image quality deterioration ratio.The digital watermark strength calculating means decides the optimumdigital watermark strength in the allowable limit value of the imagequality deterioration ratio and outputs the decided optimum digitalwatermark strength.

In the digital watermark inserting system according to the presentinvention, the restriction information of the digital watermark strengthis a limit value of a safety index against an attack. The digitalwatermark strength calculating means decides the optimum digitalwatermark strength in a range of which the robustness evaluation valueagainst the attack exceeds the limit value of the safety index andoutputs the decided optimum digital watermark strength.

In the digital watermark inserting system according to the presentinvention, the restriction information of the digital watermark is aweighting index that defines the balance of the image qualitydeterioration amount and the safety index. The digital watermarkstrength calculating means decides the ratio of the contribution of theimage quality deterioration amount and the safety index for deciding theoptimum digital watermark strength with the weighting index.

In the digital watermark inserting system according to the presentinvention, the digital watermark characteristic means (131, FIG. 7) hasa digital watermark characteristic parameter table generating means(132, FIG. 7) for generating the digital watermark characteristicparameter table that is input to the digital watermark characteristiccalculating means (131, FIG. 7).

In the digital watermark inserting system according to the presentinvention, the digital watermark characteristic parameter tablegenerating means (132, FIG. 7) has a digital watermark inserting means(200, FIG. 12) for converting input embedding information into digitalwatermark information, inserting the digital watermark information intothe input image with the input digital watermark strength, andgenerating the digital watermark inserted image, an attack imagegenerating means (201, FIG. 12) for adjusting the strength of an attackwith an input attack parameter against the digital watermark insertedimage, and generating an attacked image, a digital watermark detectingmenas (202, FIG. 12) for detecting a digital watermark from the attackedimage and outputting the detected result, an image quality deteriorationamount calculating means (203, FIG. 12) for comparing the input imagewith the digital watermark inserted image, calculating an image qualitydeterioration amount caused by the inserted digital watermark with thecompared result, and outputting the calculated image qualitydeterioration amount, a categorizing means (204, FIG. 12) forcalculating a feature amount of the input image, categorizing the inputimage with the calculated feature amount, and outputting a categoryindex corresponding to the categorized result, and a digital watermarkcharacteristic parameter table calculating means (205, FIG. 12) forreceiving the detected result of the digital watermark, the digitalwatermark strength, the attack parameter, the image qualitydeterioration amount, and the category index, totaling the detectedresults of each of combinations of the category index, the digitalwatermark strength, and the attack parameter, obtaining a detectionratio as the totaled result, totaling an image quality deteriorationamount of each of combinations of the category index and the digitalwatermark strength, obtaining a image quality deterioration ratio as thetotaled result, calculating a digital watermark characteristic parametertable using the detection ratio and the image quality deteriorationratio, and outputting the calculated digital watermark characteristicparameter table.

In the digital watermark inserting system according to the presentinvention, the digital watermark characteristic parameter tablecalculating means has a detection ratio calculating means (300, FIG. 13)for totaling a detected result of the digital watermark information foreach of the attack parameter, the digital watermark strength, and thecategory index, calculating detection ratio data with the totaledresult, and outputting the calculated detection ratio data, an imagequality deterioration ratio calculating means (301, FIG. 13) fortotaling an image quality deterioration amount for each of the categoryindex and the digital watermark strength and outputting the resultantstatistic amount as an image quality deterioration ratio, a digitalwatermark characteristic extracting means (302, FIG. 13) for calculatingdetection ratio descriptive information describing the variation of thedetection ratio data against the digital watermark strength, the attackparameter, and the category index and image quality deterioration ratiodescriptive information describing the variation of the image qualitydeterioration ratio and outputting the detection ratio descriptiveinformation and the image quality deterioration ratio descriptiveinformation, and a data combining means (303, FIG. 13) for combining thedigital watermark strength, the category index, the image qualitydeterioration ratio descriptive information, and the detection ratiodescriptive information, generating a digital watermark characteristicparameter table as the combined result, and outputting the generateddigital watermark characteristic parameter table.

In the digital watermark inserting system according to the presentinvention, the digital watermark characteristic extracting means has adetection ratio characteristic extracting means (320, FIG. 14) forapproximating a function representing the variation of the detectionratio data against the attack parameter for each of the category indexand the digital watermark strength with a curve/curved surface,calculating a detection ratio characteristic parameter describing thecurve/curved surface, and outputting the calculated detection ratiocharacteristic parameter as the detection ration descriptiveinformation. The image quality deterioration ratio is output as theimage quality deterioration ratio descriptive information.

In the digital watermark inserting system according to the presentinvention, the detection ratio characteristic parameter calculated bythe detection ratio characteristic extracting means (320, FIG. 14) is adetection ratio curve parameter for a single attack.

In the digital watermark inserting system according to the presentinvention, the detection ratio characteristic parameter calculated bythe detection ratio characteristic extracting means (320, FIG. 14) iscomposed of a detection ratio curve parameter for a single attack and anattack correlation curved surface parameter describing the correlationof single attacks.

In the digital watermark inserting system according to the presentinvention, the digital watermark characteristic extracting means (302,FIG. 13) has a detection ratio characteristic calculating means (340,FIG. 15) for approximating a function that represents the variation ofthe detection ratio data against the attack parameter for each of thecategory index and the digital watermark strength with a curve/curvedsurface, calculating a detection ratio characteristic parameter thatrepresents the curve/curved surface, approximating the variation of thedetection ratio characteristic parameter against the digital watermarkstrength with a curve, obtaining a detection ratio characteristicgeneral parameter that describes the curve, and outputting the detectionratio characteristic general parameter as the detection ratiodescriptive information, and an image quality deterioration ratiocharacteristic extracting means (341, FIG. 15) for approximating thevariation of the image quality deterioration ratio against the digitalwatermark strength with a curve, calculating an image qualitydeterioration ratio curve parameter that describes the curve, andoutputting the image quality deterioration ratio curve parameter as theimage quality deterioration ratio descriptive information.

In the digital watermark inserting system according to the presentinvention, the detection ratio characteristic parameter and thecalculated detection ratio characteristic general parameter calculatedby the detection ratio characteristic calculating means (340, FIG. 15)are a detection ratio curve parameter for a single attack and adetection ratio curve general parameter for a single attack,respectively.

In the digital watermark inserting system according to the presentinvention, the detection ratio characteristic parameter calculated bythe detection ratio characteristic calculating means (340, FIG. 15) iscomposed of a detection ratio curve parameter for a single attack and anattack correlation curved surface parameter that describes thecorrelation of single attacks. The detection ratio characteristicgeneral parameter calculated by the detection ratio characteristiccalculating means (340, FIG. 15) is composed of a detection ratio curvegeneral parameter for a single parameter and an attack correlation curedsurface general parameter.

These and other objects, features and advantages of the presentinvention will become more apparent in light of the following detaileddescription of a best mode embodiment thereof as illustrated in theaccompanying drawings.

BRIEF DESCRIPTION OF DRAWINGS

FIG. 1 is a block diagram showing the structure of a conventionaldigital watermark inserting system;

FIG. 2 is a block diagram showing the structure of a conventionaldigital watermark characteristic table generating unit;

FIG. 3 is a block diagram showing the structure of a digital watermarkcharacteristic table generating portion 2201;

FIG. 4 is a block diagram showing a first example of the structure ofthe system according to the present invention;

FIG. 5 is a block diagram showing a first example of the structure of adigital watermark characteristic calculating portion 104 according tothe present invention;

FIG. 6 is a block diagram showing a second example of the structure ofthe digital watermark characteristic calculating portion 104 accordingto the present invention.

FIG. 7 is a block diagram showing the structure of a second embodimentof the present invention;

FIG. 8 is a block diagram showing the structure of a third embodiment ofthe present invention;

FIGS. 9A to 9D are graphs showing examples of a detection ratioapproximating method;

FIGS. 10A to 10D are graphs showing examples of a digitizing process ina robustness evaluation value calculating method according to thepresent invention;

FIGS. 11A to 11D are graphs showing examples of an approximating methodfor an image quality deterioration ratio and a detection ratio curveparameter according to the present invention;

FIG. 12 is a block diagram showing the structure of a digital watermarkcharacteristic parameter table generating unit 132 shown in FIG. 7according to the present invention;

FIG. 13 is a block diagram showing the structure of a digital watermarkcharacteristic parameter table calculating portion 205 shown in FIG. 12according to the present invention;

FIG. 14 is a schematic diagram showing a first example of the structureof a digital watermark characteristic extracting portion 302 shown inFIG. 13 according to the present invention;

FIG. 15 is a schematic diagram showing a second example of the structureof the digital watermark characteristic extracting portion 302 shown inFIG. 13 according to the present invention;

FIGS. 16A to 16D are graphs showing real examples of a detection ratiocurve calculated according to the present invention;

FIGS. 17A to 17C are graphs showing the relation of a detection ratiocurve and a parameter according to the present invention;

FIGS. 18A to 18D are graphs showing real examples of a detection ratiocurve calculated according to the present invention;

FIGS. 19A and 19B are graphs showing real examples of an image qualitydeterioration curve calculated according to the present invention;

FIGS. 20A and 20B are graphs showing real examples of an approximationcurve of a detection ratio curve parameter calculated against adetection ratio curve parameter of the graph shown in FIG. 16B;

FIGS. 21A and 21B are graphs showing real examples of an approximationcurve of a detection ratio curve parameter calculated against adetection ratio curve parameter of the graphs shown in FIG. 16C;

FIGS. 22A to 22C are graphs showing real examples of an approximationcurve of a detection ratio curve parameter calculated against adetection ratio curve parameter of the graph shown in FIG. 16D;

FIG. 23 is a graph showing a real example of an attack correlation valuecalculated according to the present invention;

FIGS. 24A and 24B are graphs showing real examples of an attackcorrelation value and an attack correlation curved surface calculatedaccording to the present invention; and

FIGS. 25A to 25C are graphs showing examples of an approximation curveof an attack correlation curved surface parameter calculated against anattack correlation curved surface parameter of the graph shown in FIG.24B.

DESCRIPTION OF PREFERRED EMBODIMENTS

Next, with reference to the accompanying drawings, embodiments of thepresent invention will be described.

[First Embodiment]

FIG. 4 is a block diagram showing the structure of a digital watermarkinserting system according to a first embodiment of the presentinvention. The digital watermark inserting system according to the firstembodiment comprises a categorizing portion 103, a digital watermarkcharacteristic calculating portion 104, a digital watermark strengthcalculating portion 100, and a digital watermark inserting portion 102.The digital watermark characteristic calculating portion 104 uses arobustness evaluation value calculation parameter. The digital watermarkstrength calculating portion 100 calculates digital watermark strengthwith the digital watermark strength restriction information.

The categorizing portion 103 calculates a feature amount of an inputimage, categorizes the image based on the obtained feature amount, andoutputs the categorized result as a category index to the digitalwatermark characteristic calculating portion 104. The digital watermarkcharacteristic calculating portion 104 calculates an image qualitydeterioration ratio and a robustness evaluation value for the digitalwatermark strength that is output from the digital watermark strengthcalculating portion 100 based on the category index that is output fromthe categorizing portion 103 and a robustness evaluation valuecalculation parameter that is input by the user and outputs thecalculated image quality deterioration ratio and robustness evaluationvalue to the digital watermark strength calculating portion 100.

In addition, the digital watermark strength calculating portion 100outputs various values of the digital watermark strength based on thedigital watermark strength restriction information to the digitalwatermark characteristic calculating portion 104, decides the optimumdigital watermark strength based on the image quality deteriorationratio and robustness evaluation value (against the digital watermarkstrength) received from the digital watermark characteristic calculatingportion 104 and based on the digital watermark strength restrictioninformation that is input by the user, and outputs the decided optimumdigital watermark strength to a digital watermark inserting portion 102.

The digital watermark inserting unit 102 converts embedding data into adigital watermark, inserts the digital watermark into the image with theoptimum digital watermark strength that is received from the digitalwatermark strength calculating portion 100, and outputs the resultantimage as a digital watermark inserted image. Next, the operation of thedigital watermark inserting system shown in FIG. 4 will be described.

An input image is supplied to the categorizing portion 103. Theoperation of the categorizing portion 103 is the same as that of thedigital watermark inserting system shown in FIG. 1. The categorizingportion 103 categorizes the input image and outputs the categorizedresult as a category index. The feature amount used in the categorizingportion 103 is for example an activity of an image, the mean value ofJND, the number of colors for use, entropy, or the like. Alternatively,the category may be used to distinguish image types such as medicalimages, CG, animation, and so forth. With the feature amount of theimage, the image type may be automatically predicted. Moreover, the usermay explicitly designate the category of an input image.

The category index that is output from the categorizing portion 103 isinput to the digital watermark characteristic calculating portion 104.The digital watermark characteristic calculating portion 104 calculatesan image quality deterioration ratio and a robustness evaluation valuecorresponding to the digital watermark strength that is output from thedigital watermark strength calculating portion 100 with an image qualitydeterioration ratio descriptive information and the detection ratiodescriptive information stored in a storing unit shown in FIG. 5 (thestoring unit will be described later) and outputs the calculated imagequality deterioration ratio and robustness evaluation value to thedigital watermark strength calculating portion 100.

The image quality deterioration ratio descriptive information may be animage quality deterioration ratio or an image quality deteriorationratio curve parameter that is a parameter of a curve that approximatesthe variation of an image quality deterioration ratio against a digitalwatermark strength (hereinafter, the curve is referred to as imagequality deterioration ratio curve).

On the other hand, the detection ratio descriptive information may be aparameter that describes a curve/curved surface that approximates thevariation of a detection ratio against an attack parameter (hereinafter,the curve/curved surface is referred to as detection ratio curve/curvedsurface) or a parameter of a curve that approximates the variation of adetection ratio characteristic parameter against a digital watermarkstrength (hereinafter, this curve and this parameter are referred to asdetection ratio characteristic parameter curve and detection ratiocharacteristic general parameter, respectively).

In the case of a single attack composed of a single process, thedetection ratio characteristic parameter is a detection ratio curveparameter that is a curve parameter that approximates the variation of adetection ratio against an attack parameter. In the case of a complexattack that is a combination of a plurality of processes, the detectionratio characteristic parameter is composed of a detection ratio curveparameter against a single attack and an attack correlation curvedsurface parameter that approximates the correlation values of singleattacks.

In the case of a single attack, the detection ratio characteristicgeneral parameter is a detection ratio curve general parameter that is acurve parameter that approximates the variation of a detection ratiocurve parameter against a digital watermark strength. In the case of acomplex attack, the detection ratio characteristic general parameter iscomposed of a detection ratio curve general parameter against a singleattack and an attack correlation curved surface parameter that is acurve parameter that approximates the variation of an attack correlationcurved surface parameter against a digital watermark strength.

The structure and operation of the digital watermark characteristiccalculating portion 104 depend on whether the image qualitydeterioration ratio descriptive information is an image qualitydeterioration ratio or an image quality deterioration ratio curveparameter. In addition, the structure and operation of the digitalwatermark characteristic calculation portion 104 depend on whether thedetection ratio descriptive information is a detection ratiocharacteristic parameter or a detection ratio characteristic generalparameter. Moreover, the structure and operation of the digitalwatermark characteristic calculation portion 104 depend on whether theconsidered attack is a single attack or a complex attack. The structureand operation of the digital watermark characteristic calculatingportion 104 in these cases will be described later.

A robustness evaluation value that is output from the digital watermarkcharacteristic calculating portion 104 may be a robustness evaluationvalue for one attack or a statistic amount of which a statistic processsuch as weighted means method is performed for robustness evaluationvalues calculated for various single/complex attacks. When a means valueis calculated, weights to individual attacks may be input as arobustness evaluation value calculation parameter to the digitalwatermark characteristic calculating portion 104. The user may vary theweights.

The operation of the digital watermark strength calculating portion 100is the same as the operation of the digital watermark inserting systemshown in FIG. 1. An input image is supplied to the system. Thecategorizing portion 103 calculates a category index of the input imageand then calculates a digital watermark strength that maximizes thevalue of the formula 1 as the optimum digital watermark strength.

The optimum digital watermark strength that is output from the digitalwatermark strength calculating portion 100 is input to the digitalwatermark inserting portion 102. The operation of the digital watermarkinserting portion 102 is the same as that of the conventional system.The digital watermark inserting portion 102 converts input embeddingdata into a digital watermark, inserts the digital watermark into theinput image, and outputs the resultant image as a digital watermarkinserted image.

In the above-described digital watermark inserting system, an imagequality deterioration limit value D0 may be changed as digital watermarkstrength restriction information. When the user does not designate thelimit value D0, a predetermined value is used as a default value. Whenthe user designates the limit value D0, it is used. Thus, the user canadjust image quality deterioration caused by an inserted digitalwatermark.

In the above-described digital watermark inserting system, a safetylimit value V0 against an attack may be changed as digital watermarkstrength restriction information. When the user does not designate thelimit value V0, a predetermined value is used as a default value. Whenthe user designates the limit value V0, it is used. Thus, the user canadjust the robustness of a digital watermark against an attack.

In the above-described digital watermark inserting system, the parameter“a” of the formula (1) that is a weighting index that allows an imagequality deterioration amount of an objective function and a safety indexto be balanced can be changed as digital watermark strength restrictioninformation. When the user does not designate the parameter “a”, apredetermined value is used as a default value. When the user designatesthe parameter “a”, it is used. Thus, the user can selectively emphasizethe deterioration of the image quality or the robustness against anattack.

In addition, the image quality deterioration permission limit value D0,the attack safety index limit value V0, and the weighting index “a” foremphasizing either the image quality deterioration amount or the safetyindex may be changed as digital watermark strength restrictioninformation. The user can designate these values. When the user does notdesignate these values, predetermined values are used as default values.Thus, the user can freely adjust an image quality deteriorationpermission limit value, an attack safety index limit value, and thebalance between image quality deterioration and an attack.

Next, with reference to FIG. 4, the structure and operation of thedigital watermark characteristic calculating portion 104 in the casethat the image quality deterioration ratio descriptive information is animage quality deterioration ratio and the detection ratio descriptiveinformation is a detection ratio characteristic parameter will bedescribed.

FIG. 5 is a block diagram showing the structure of the digital watermarkcharacteristic calculating portion 104. A storing unit 101 selects adigital watermark characteristic parameter table corresponding to acategory index that is received from the categorizing portion 103 shownin FIG. 4 and outputs an image quality deterioration ratio correspondingto a digital watermark strength that is received from the digitalwatermark strength calculating portion 100 shown in FIG. 4 to thedigital watermark strength calculating portion 100. In addition, thestoring unit 101 outputs a detection ratio characteristic parameter to arobustness evaluation value calculating portion 105. The robustnessevaluation value calculating portion 105 obtains a detection ratiocurve/curved surface that approximates the relation of a detection ratioand an attack parameter with the detection ratio characteristicparameter that is received from the storing unit 101, calculates arobustness evaluation value based on a robustness evaluation valuecalculation parameter that is input by the user, and outputs thecalculated robustness evaluation value to the digital watermark strengthcalculating portion 100 shown in FIG. 4.

Next, the operation of the digital watermark characteristic calculatingportion shown in FIG. 5 will be described. First of all, the operationof the digital watermark characteristic calculating portion in the caseof a single attack will be described. In this case, the detection ratiocharacteristic parameter is a detection ratio curve parameter.

A category index that is output from the categorizing portion 103 shownin FIG. 4 is input to the storing unit 101. The storing unit 101 storesdigital watermark characteristic parameter tables for individualcategory indexes. Each of the digital watermark characteristic parametertables describe the relation between a digital watermark strength, animage quality deterioration ratio, and a detection ratio curveparameter. Table 2 shows a digital watermark characteristic parametertable for a category index k.

TABLE 2 Digital Image quality watermark deterioration Detection ratiocurve strength ratio parameter S(1) D(k, 1) c1(k, 1), c2(k, 1), . . .S(2) D(k, 2) c1(k, 2), c2(k, 2), . . . . . . . . . . . . S(M) D(k, M)c1(k, M), c2(k, M), . . .

In this example, when a category index and a digital watermark strengthindex are denoted by k and m, respectively, a detection ratio curveparameter is denoted by c1(k, m), c2(k, m), . . .

For example, in the case where the detection ratio varies with theattack parameter as shown in FIG. 9A (where x=0 corresponds to noattack), it is approximated with a logistic curve expressed by thefollowing formula as shown in FIG. 9B: $\begin{matrix}{{r(x)} = \frac{1}{1 + {\exp \left( {c_{1}\left( {x + c_{2}} \right)} \right)}}} & (7)\end{matrix}$

Alternatively, it is approximated with a graph of broken lines expressedby the following formula as shown in FIG. 9C: $\begin{matrix}{{r(x)} = {\frac{1}{c_{1} - c_{2}}\left( {x - c_{2}} \right)}} & (8)\end{matrix}$

Alternatively, it is approximated with a graph of broken lines expressedby the following formula as shown in FIG. 9D: $\begin{matrix}{{r(x)} = \left\{ \begin{matrix}{{{{- \frac{1 - c_{2}}{c_{2}}}x} + 1}\quad} & {\left( {0 \leq x < c_{1}} \right)\quad} \\{{- \frac{c_{2}}{c_{3} - c_{1}}}\left( {x - c_{3}} \right)} & \left( {c_{1} \leq x \leq c_{3}} \right)\end{matrix} \right.} & (9)\end{matrix}$

In the formula (7), c1 and c2 are detection ratio curve parameters. Inthe formula (8), c1 and c2 are detection ratio curve parameters. In theformula (9), c1, c2, and c3 are detection ratio curve parameters.

Feature amounts used as parameters are not limited as long as they havethe same degree of freedom. For example, instead of c1 and c2 of theformula (8), the slope of a straight line and the value of an interceptmay be used as detection ratio curve parameters. In addition, otherapproximation curves may be used. For example, a fraction functionexpressed by the following formula may be used: $\begin{matrix}{{r(x)} = \frac{1}{1 + {c_{1}x} + {c_{2}x^{2}}}} & (10)\end{matrix}$

Alternatively, an exponential function expressed by the followingformula may be used:

r(x)=exp(−c ₁(x−c ₂))  (11)

In the function expressed by the formula (10), c1 and c2 are detectionratio curve parameters. In the function expressed by the formula (11),c1 and c2 are detection ratio curve parameters. One of these curves canbe properly selected for each attack.

In the example, the case that the state of x=0 represents that there isno attack was described. Alternatively, the above-described curve canalso be applied in other cases by shifting or inverting it.

The storing unit 101 has a digital watermark characteristic table shownin Table 2. The storing unit 101 selects a digital watermark parametercharacteristic table corresponding to a category index k that isreceived from the categorizing portion 103. A digital watermark strengths(m) is input to the storing unit 101 from the digital watermarkstrength calculating portion 100 and then outputs an image qualitydeterioration ratio D(k, m) to the digital watermark strengthcalculating portion 100. On the other hand, the storing unit 101 outputsdetection ratio characteristic parameters c1(k, m), c2(k, m), . . . tothe robustness evaluation value calculating portion 105.

The robustness evaluation value calculating portion 105 obtains a curvethat represents the relation of a detection ratio and an attackparameter with the detection ratio curve parameters c1(k, m), c2(k, m),. . . that are received from the storing unit 101 and calculates arobustness evaluation value. The robustness evaluation value calculatingportion 105 has designated types of curves for individual attacks. Thus,the robustness evaluation value calculating portion 105 decides theshape of the curve based on an input detection ratio curve parametervalue. Alternatively, the robustness evaluation value calculatingportion 105 may select one of several types of curves. In this case, anindex that designates the type of a curve is contained in the detectionratio curve parameter. Hereinafter, a phrase “obtaining a curve/curvedsurface” represents that the shape of a curve is decided based on aninput parameter.

With input detection ratio curve parameters, a detection ratio curvethat represents the relation of an attack parameter and a detectionratio that are expressed by the formula (7) is obtained by the followingformula: $\begin{matrix}{{r\left( {k,m,x} \right)} = \frac{1}{1 + {\exp \left( {{c_{1}\left( {k,m} \right)} + {{c_{2}\left( {k,m} \right)}x}} \right)}}} & (12)\end{matrix}$

Next, with the formulas (4) and (5), a robustness evaluation value V(k,m) is calculated. The robustness evaluation value calculating portion105 has a storing means that stores data of a weighting function w(x)and a threshold value a.

When a robustness evaluation value is really calculated, as shown inFIGS. 10A to 10D, a detection ratio r(k, m, x) and a weighting functionw(x) are digitized and calculated using the following formula:$\begin{matrix}{{V\left( {k,m} \right)} = {\sum\limits_{k = 1}^{H}{{w\left( {x_{0} + {h\quad \Delta \quad x}} \right)}{r\left( {k,m,{x_{0} + {h\quad \Delta \quad x}}} \right)}\Delta \quad x}}} & (13)\end{matrix}$

or the following formula: $\begin{matrix}{{V\left( {k,m} \right)} = {\frac{1}{L}{\sum\limits_{k = 1}^{H}{{T\left( {{r\left( {k,m,{x_{0} + {h\quad \Delta \quad x}}} \right)},\alpha} \right)}\Delta \quad x}}}} & (14)\end{matrix}$

Alternatively, a function may be approximated and integrated using suchas Simpson's formula or trapezoid formula.

In addition, before a digital watermark is inserted, the user can changethe weighting function and the threshold value. In FIG. 4, theevaluation value calculation parameter is data that represents aweighting function or a threshold value. When the weighted mean ofrobustness evaluation values of a plurality of attacks is obtained andoutput as a robustness evaluation value to the digital watermarkstrength calculating portion 100, the evaluation value calculationparameter is a weighting coefficient. When the user does not designatethese values, predetermined values stored in the storing unit are usedas default values. When the user designates these values, they are used.With the changed weighting function and threshold value or weightingcoefficients of individual attacks, the robustness evaluation value iscalculated.

Next, an attack correlation curved surface will be described.Thereafter, the operation of the digital watermark characteristiccalculating portion shown in FIG. 5 in the case of a complex attack willbe described. In this case, the detection ratio characteristic parameteris composed of a detection ratio curve parameter of each single attackcomposing the complex attack and an attack correlation curved surfaceparameter.

The attack correlation curved surface is a curved surface thatrepresents the relation of detection ratios of individual single attackscomposing the complex attack and the detection ratio of the complexattack. In the following example, the operation of the digital watermarkcharacteristic calculating portion in the case of a complex attackcomposed of two single attacks (hereinafter referred to as attack 1 andattack 2) will be described. However, it should be noted that thepresent invention can be applied to a complex attack composed of morethan two single attacks. The detection ratio of the attack 1 against theattack parameter value that is x1 is denoted by r1(x1). The detectionratio of the attack 2 against the attack parameter value that is x2 isdenoted by r2(x2). The detection ratio of the complex attack is denotedby r1,2(x1, x2).

When attacks are combined, if there is no synergism effect thereof, itis predicted that the detection ratio is expressed by the followingformula:

r_(1,2)(x₁,x₂)≈r₁(x₁)r₂(x₂)  (15)

On the other hand, if there is a synergist effect, the formula (15) isnot satisfied. In this case, a function that represents the synergismeffect of the combination of attacks is expressed by the followingformula: $\begin{matrix}{{z_{1,2}\left( {x_{1},x_{2}} \right)} = \frac{r_{1,2}\left( {x_{1},x_{2}} \right)}{{r_{1}\left( x_{1} \right)}{r_{2}\left( x_{2} \right)}}} & (16)\end{matrix}$

The formula (16) is referred to as attack correlation value. A curvedsurface that approximates the variation of an attack correlation valueagainst an attack parameter is referred to as attack correlation curvedsurface. If there is no synergism effect of a combination of attacks,z1,2(x1, x2) is always 1.

Assuming that the state of which both attack parameters x1 and x2 are 0represents no attacks, the following formula is satisfied:$\begin{matrix}\left\{ \begin{matrix}{{r_{1,2}\left( {x_{1},0} \right)} = {r_{1}\left( x_{1} \right)}} \\{{r_{1,2}\left( {0,x_{2}} \right)} = {r_{2}\left( x_{2} \right)}}\end{matrix} \right. & (17)\end{matrix}$

In addition, basically, the following relation is satisfied:

r ₁(0)=r ₂(0)=1  (18)

Thus, the following relation is satisfied:

z _(1,2)(x ₁, 0)=z _(1,2)(0, x ₂)=1  (19)

Next, the operation of the digital watermark characteristic portionshown in FIG. 5 will be described.

A category index that is output from the categorizing portion 103 shownin FIG. 4 is input to the storing unit 101. For each category index, inaddition to the table 2, which describes the relation between a digitalwatermark strength, an image quality deterioration ratio, and adetection ratio curve parameter against a single attack, the storingunit 101 stores a digital watermark characteristic parameter table thatdescribes the relation between a digital watermark strength and anattack correlation curved surface parameter for each category index k.Table 3 shows the digital watermark characteristic parameter table.

TABLE 3 Attack correlation curved Digital watermark strength surfaceparameter s(1) q1(k, 1), q2(k, 1), . . . s(2) q1(k, 2), q2(k, 2), . . .. . . . . . s(M) q1(k, M), q2(k, M), . . .

In Table 3, k is a category index; m is a digital watermark strengthindex; and q1(k, m), q2(k, m), . . . are attack correlation curvedsurface parameters.

For example, in each single attack composing a complex attack, when thestate of which the value of the attack parameter is 0 represents thatthere is no attack, it can be approximated by functions expressed by thefollowing formulas: $\begin{matrix}{{z_{1,2}\left( {x_{1},x_{2}} \right)} = \frac{1}{1 + {q_{1}x_{1}^{q2}x_{2}^{q3}}}} & (20)\end{matrix}$

where q1, q2, and q3 are attack correlation curved surface parameters:$\begin{matrix}{{z_{1,2}\left( {x_{1},x_{2}} \right)} = {1 - \frac{1}{\left( {1 + {\exp \left( {q_{1} + {q_{2}x_{1}}} \right)}} \right)\left( {1 + {\exp \left( {q_{3} + {q_{4}x_{2}}} \right)}} \right)}}} & (21)\end{matrix}$

where q1, q2, q3, and q4 are attack correlation curved surfaceparameters. A curve to be used depends on a combination of types ofattacks. Thus, a proper curve is selected for each complex attack.

The storing unit 101 stores the digital watermark characteristicparameter tables shown in Tables 2 and 3. In accordance with thecategory index k that is output from the categorizing portion 103, thestoring unit 101 selects a proper digital watermark parametercharacteristic table. When a digital watermark strength s(m) is input tothe storing unit 101 from the digital watermark strength calculatingportion 100, the storing unit 101 outputs an image quality deteriorationratio D(k, m) to the digital watermark strength calculating portion 100.On the other hand, the storing unit 101 outputs detection ratiocharacteristic parameters c1(k, m), c2(k, m), . . . for a single attackand attack correlation curved surface parameters q1(k, m), q2(k, m), . .. to the robustness evaluation value calculating portion 105.

The robustness evaluation value calculating portion 105 obtains anattack correlation curved surface with the attack correlation curvedsurface parameters q1(k, m), q2(k, m), . . . that are input from thestoring unit 101. In other words, when an approximation is performedwith the formula (20), the attack correlation curved surface is obtainedby the following formula: $\begin{matrix}{{z_{1,2}\left( {k,m,x_{1},x_{2}} \right)} = \frac{1}{1 + {{q_{1}\left( {k,m} \right)}x_{1}^{q_{2}{({k,m})}}x_{2}^{q_{1}{({k,m})}}}}} & (22)\end{matrix}$

Next, with the detection ratio curve parameters c1(k, m), c2(k, m), . .. for single attacks, a detection ratio curve for each single attackcomposing the complex attack is obtained. With the obtained attackcorrelation curved surface and the detection ratio curve, the detectionratio curved surface for the complex attack is obtained by the followingformula:

r _(1,2)(k,m,x ₁ ,x ₂)=r ₁(k,m,x ₁)r ₂(k,m,x ₂)z _(1,2)(x ₁ ,x ₂)  (23)

With the following formulas that are equivalent to the formulas (4) and(5) for single attacks, a robustness evaluation value is calculated.When a robustness evaluation value is really calculated using the followformulas, as with the case for a single attack, a detection ratio, anattack correlation curved surface, and a weighting function aredigitized and calculated: $\begin{matrix}\begin{matrix}{{V\left( {k,m} \right)} = {\int{\int{{w_{1,2}\left( {x_{1},x_{2}} \right)}{r_{1,2}\left( {k,m,x_{1},x_{2}} \right)}{x_{1}}{x_{2}}}}}} \\{= {\int{\int{{w_{1,2}\left( {x_{1},x_{2}} \right)}{r_{1}\left( {k,m,x_{1}} \right)}{r_{2}\left( {k,m,x_{2}} \right)}{z_{1,2}\left( {x_{1},x_{2}} \right)}{x_{1}}{x_{2}}}}}}\end{matrix} & (24)\end{matrix}$

$\begin{matrix}\begin{matrix}{{V\left( {k,m} \right)} = {\frac{1}{L}{\int{\int{{T\left( {{r_{1,2}\left( {k,m,x_{1},x_{2}} \right)},\alpha} \right)}{x_{1}}{x_{2}}}}}}} \\{{= {\frac{1}{L}{\int{\int{{T\left( {{{r_{1}\left( {k,m,x_{1}} \right)}{r_{2}\left( {k,m,x_{2}} \right)}{z_{1,2}\left( {x_{1},x_{2}} \right)}},\alpha} \right)}{x_{1}}}}}}},{x_{2}}}\end{matrix} & (25)\end{matrix}$

The robustness evaluation value calculating portion 105 has a storingmeans that stores data of a weighting function w1,2(x1, x2) and athreshold value α.

When the weighting function w1,2(x1, x2) is a separable for the attackparameters x1 and x2 and the attack correlation curved surface z1,2(x1,x2) is the linear sum of a separable function as with the formula (21)(namely, the following formulas are satisfied):

w _(1,2)(x ₁ ,x ₂)=w ₁(x ₁)w ₂(x ₂)  (26) $\begin{matrix}{{w_{1,2}\left( {x_{1},\quad x_{2}} \right)} = {{w_{1}\left( x_{1} \right)}{w_{2}\left( x_{2} \right)}}} & (26) \\{{z_{1,2}\left( {x_{1},\quad x_{2}} \right)} = {\sum\limits_{i}\quad {{Z_{1}}^{(i)}\quad \left( x_{1} \right){{z_{2}}^{(i)}\left( x_{2} \right)}}}} & (27)\end{matrix}$

the right side of the formula (24) can be expressed as follows:$\begin{matrix}{\sum\limits_{i}{\int{{w_{1}\left( x_{1} \right)}{r_{1}\left( {k,m,x_{1}} \right)}{z_{1}^{(i)}\left( {k,m,x_{1}} \right)}{x_{1}}{\int{{w_{2}\left( x_{2} \right)}{r_{2}\left( {k,m,x_{2}} \right)}{z_{2}^{(i)}\left( {k,m,x_{2}} \right)}{x_{2}}}}}}} & (28)\end{matrix}$

Thus, after an integrating calculation is performed for each singleattack, the results are multiplied and added. Thus, the robustnessevaluation value V(k, m) can be calculated. Consequently, thecalculation amount can be remarkably reduced. In particular, in the caseof z1,2(x1, x2)=1, only by calculating the product of the robustnessevaluation values of single attacks, the robustness evaluation value ofa complex attack can be derived.

Next, with reference to FIG. 6, the structure and operation of thedigital watermark characteristic general parameter 104 shown in FIG. 4in the case that information that represents an image qualitydeterioration ratio is an image quality deterioration ratio curveparameter and information that represents a detection ratiocharacteristic general parameter will be described.

FIG. 6 is a block diagram showing an example of the structure of thedigital watermark characteristic calculating portion according to thefirst embodiment of the present invention.

A storing unit 171 outputs an image quality deterioration ratio curveparameter corresponding to the category index that is received from thecategorizing portion 103 shown in FIG. 3 to an image qualitydeterioration ratio calculating portion 172. In addition, the storingunit 171 outputs a detection ratio characteristic general parameter to arobustness evaluation value calculating portion 173.

The image quality deterioration ratio calculating portion 172 obtains animage quality deterioration ratio curve that represents the variation ofan image quality deterioration ratio against a digital watermarkstrength with the image quality deterioration ratio curve parameter thatis received from the storing unit 171, calculates an image qualitydeterioration ratio corresponding to a digital watermark strength thatis received from the digital watermark strength calculating portion 100and outputs the calculated image quality deterioration ratio to thedigital watermark strength calculating portion 100.

The robustness evaluation value calculating portion 173 obtains adetection ratio curve/curved surface with the detection ratiocharacteristic general parameter that is received from the storing unit171 and the digital watermark strength that is received from the digitalwatermark strength calculating portion 100 shown in FIG. 4. In addition,the robustness evaluation value calculating portion 173 calculates arobustness evaluation value based on a robustness evaluation valuecalculation parameter that is input by the user, and outputs thecalculated robustness evaluation value to the digital watermark strengthcalculating portion 100 shown in FIG. 4.

Next, the operation of the digital watermark characteristic calculatingportion shown in FIG. 6 will be described.

First of all, the operation of the digital watermark characteristiccalculating portion in the case of a single attack will be described. Inthis case, the detection ratio characteristic general parameter is adetection ratio curve general parameter. A category index that is outputfrom the categorizing portion 103 shown in FIG. 4 is input to thestoring unit 171. The storing unit 171 stores a digital watermarkcharacteristic parameter table that describes the relation between acategory index, an image quality deterioration ratio curve parameter,and a detection ratio curve general parameter as shown in Table 4.

TABLE 4 Image quality deterioration Detection ratio ratio curve curvegeneral Category index parameter parameter 1 b1(1), b2(1), . . . p1(1),p2(1), . . . 2 b1(2), b2(2), . . . p1(2), p2(2), . . . . . . . . . . . .K b1(K), b2(K), . . . p1(K), p2(K), . . .

where k is a category index; b1(k), b2(k), . . . are image qualitydeterioration ratio curve parameters; and p1(k), p2(k), . . . aredetection ratio curve general parameters.

The image quality deterioration ratio is approximated by for example agraph of broken lines or a polynomial. When an image qualitydeterioration ratio D(s) varies against a digital watermark strength sas shown in FIG. 11A, the image quality deterioration ratio D(s) isapproximated as shown in FIG. 11B. For example, the image qualitydeterioration ratio D(s) is approximated with a quadratic functionexpressed by the following formula, b1, b2, and b3 are image qualitydeterioration ratio curve parameters:

D(s)=b ₁ +b ₂ s+b ₃ s ²  (29)

A detection ratio curve parameter is approximated with for example agraph of broken lines or a polynomial. For example, when a detectionratio curve parameter ci varies against a digital watermark strength asshown in FIG. 11C, the detection ratio curve parameter is approximatedas shown in FIG. 11D. For example, when the detection ratio curveparameter is approximated with a quadratic function given by thefollowing formula, p1, p2, and p3 are detection ratio curve generalparameters:

c _(i)(k,s)=p ₁ +p ₂ s+p ₃ s ²  (30)

The storing unit 171 stores a digital watermark characteristic tableshown in Table 4. When a category index is input to the storing unit 171from the categorizing portion 103, the storing unit 171 outputs imagequality deterioration ratio curve parameters b1(k), b2(k), . . .corresponding thereto to the image quality deterioration ratiocalculating portion 172. In addition, the storing unit 171 outputsdetection ratio curve general parameters p1(k), p2(k), . . . to therobustness evaluation value calculating portion 173.

When the image quality deterioration ratio curve parameters are input tothe image quality deterioration ratio calculating portion 172 from thestoring unit 171, the image quality deterioration ratio calculatingportion 172 obtains an image quality deterioration ratio curve. Theimage quality deterioration calculating portion 172 calculates an imagequality deterioration ratio corresponding to a digital watermarkstrength that is received from the digital watermark strengthcalculating portion 100 shown in FIG. 4 with the obtained image qualitydeterioration curve and outputs the calculated image qualitydeterioration ratio to the digital watermark strength calculatingportion 100 shown in FIG. 4.

When the detection ratio curve general parameters are input to therobustness evaluation value calculating portion 173 from the storingunit 171, the robustness evaluation value calculating portion 173obtains a curve that represents the variation of a detection ratio curveparameter against a digital watermark strength. Thereafter, therobustness evaluation value calculating portion 173 obtains a detectionratio curve parameter corresponding to the digital watermark strengththat received from the digital watermark strength calculating portion100 shown in FIG. 4.

Next, the robustness evaluation value calculating portion 173 obtains adetection ratio curve with the obtained detection ratio curve parameter.With the formula (4) or (5), the robustness evaluation value calculatingportion 173 calculate a robustness evaluation value V(k, m). Thecalculating method used by the robustness evaluation value calculatingportion 173 is the same as that used by the robustness evaluation valuecalculating portion 105 shown in FIG. 5 in the case for a single attack.The robustness evaluation value calculating portion 173 outputs theobtained robustness evaluation value to the digital watermark strengthcalculating portion 100 shown in FIG. 4.

Next, the operation of the digital watermark characteristic calculatingportion shown in FIG. 6 in the case of a complex attack will bedescribed. In this case, the detection ratio characteristic generalparameters are composed of detection ratio curve general parameters foreach single attack composing a complex attack and attack correlationcurved surface general parameters.

The category index that is output from the categorizing portion 103shown in FIG. 4 is input to the storing unit 171. In addition to thetable that describes the relation between a category index, an imagequality deterioration ratio curve parameter, and a detection ratio curvegeneral parameter shown in Table 4, the storing unit 171 stores adigital watermark characteristic parameter table shown in FIG. 5. Thedigital watermark characteristic parameter table describes the relationbetween a category index and an attack correlation curved surfacegeneral parameter.

TABLE 5 Attack correlation curved Category index surface generalparameter 1 t1(1), t2(1), . . . 2 t1(2), t2(2), . . . . . . . . . Kt1(K), t2(K), . . .

where k is a category index; and t1(k), t2(k), . . . are attackcorrelation curved surface general parameters.

As with a detection ratio curve parameter, an attack correlation curveparameter qi is approximated with for example a graph of broken lines ora polynomial. For example, the attack correlation curve parameter ql isapproximated with a quadratic function expressed by the followingformula:

q _(i)(k,s)=t ₁ +t ₂ s+t ₃ s ²  (31)

where t1, t2, and t3 are attack correlation curved surface generalparameters.

The storing unit 171 stores the digital watermark characteristic tablesshown in Table 4 and Table 5. When a category index k is input to thestoring unit 171 from the categorizing portion 103, the storing unit 171outputs image quality deterioration ratio curve parameters b1(k), b2(k),. . . corresponding to the category index k to the image qualitydeterioration ratio calculating portion 172. On the other hand, thestoring unit 171 outputs detection ratio curve general parameters p1(k),p2(k), . . . for single attacks and attack correlation curved surfacegeneral parameters t1(k), t2(k), . . . to the robustness evaluationvalue calculating portion 173.

When the image quality deterioration ratio curve parameters are input tothe image quality deterioration ratio calculating portion 172 from thestoring unit 171, the image quality deterioration ratio calculatingportion 172 obtains an image quality deterioration ratio curve. Theimage quality deterioration ratio calculating portion 172 calculates animage quality deterioration ratio corresponding to the digital watermarkstrength that is received from the digital watermark strengthcalculating portion 100 shown in FIG. 4 with the obtained image qualitydeterioration ratio curve and outputs the calculated image qualitydeterioration ratio to the digital watermark strength calculatingportion 100 shown in FIG. 4.

When the attack correlation curved surface general parameters are inputto the robustness evaluation value calculating portion 173 from thestoring unit 171, the robustness evaluation value calculating portion173 obtains a curve that represents the variation of an attackcorrelation curved surface parameters against a digital watermarkstrength. Thereafter, the robustness evaluation value calculatingportion 173 obtains attack correlation curved surface parameterscorresponding to the digital watermark strength that is received fromthe digital watermark strength calculating portion 100 shown in FIG. 4.Next, the robustness evaluation value calculating portion 173 obtains anattack correlation curved surface with the obtained attack correlationcurved surface parameters.

When detection ratio curve general parameters for a single attack areinput to the robustness evaluation value calculating portion 173 fromthe storing unit 171, the robustness evaluation value calculatingportion 173 obtains a curve that represents the variation of thedetection ratio curve parameters against the digital watermark strength.Next, the robustness evaluation value calculating portion 173 obtainsdetection ratio curve parameters corresponding to the digital watermarkstrength that is received from the digital watermark strengthcalculating portion 100 shown in FIG. 4. Thereafter, with the obtaineddetection ratio curve parameters, the robustness evaluation valuecalculating portion 173 obtains a detection ratio curve. With theobtained attack correlation curved surface and detection ratio curve, aswith the formula (23), the robustness evaluation value calculatingportion 173 obtains a detection ratio curved surface for the complexattack.

Next, with the formulas (24) and (25), the robustness evaluation valuecalculating portion 173 calculates a robustness evaluation value V(k,m). This calculating method used in the robustness evaluation valuecalculating portion 173 is the same as that used in the robustnessevaluation value calculating portion 105 shown in FIG. 5 in the case ofa complex attack. The obtained robustness evaluation value is output tothe digital watermark strength calculating portion 100 shown in FIG. 4.

[Second Embodiment]

Next, with reference to FIG. 7, a second embodiment of the presentinvention will be described.

FIG. 7 is a block diagram showing the structure of a digital watermarkinserting system according to the second embodiment of the presentinvention. In the digital watermark inserting system shown in FIG. 7, adigital watermark characteristic calculating portion 131 is used insteadof the digital watermark characteristic calculating portion 104 of thedigital watermark inserting system shown in FIG. 4. In addition, adigital watermark characteristic parameter table generating unit 132 isconnected to the digital watermark characteristic calculating portion131. The other portions of the digital watermark inserting system shownin FIG. 7 are the same as those of the digital watermark insertingsystem shown in FIG. 4.

In the system shown in FIG. 7, the digital watermark characteristicparameter table generating unit 132 generates a digital watermarkcharacteristic parameter table. The digital watermark characteristicparameter table is output to the digital watermark characteristiccalculating portion 131 and stored in a storing unit thereof. Theoperation of the digital watermark characteristic calculating portion131 is the same as that of the digital watermark characteristiccalculating portion 104 shown in FIG. 4. The digital watermarkcharacteristic parameter table generating unit 132 will be describedlater.

[Third Embodiment]

Next, with reference to FIG. 8, a third embodiment of the presentinvention will be described.

FIG. 8 is a block diagram showing the structure of a digital watermarkinserting system according to a third embodiment of the presentinvention. In the digital watermark inserting system shown in FIG. 8, adigital watermark characteristic calculating portion 151 is used insteadof the digital watermark characteristic calculating portion 104 of thedigital watermark inserting system shown in FIG. 4. In addition, aninput unit 152 is connected to the digital watermark characteristiccalculating portion 151. A record medium unit 153 is connected to theinput unit 152. The other portions of the digital watermark insertingsystem shown in FIG. 8 are the same as those of the digital watermarkinserting system shown in FIG. 4.

In the system shown in FIG. 8, a unit equivalent to the digitalwatermark characteristic parameter table generating unit 132 shown inFIG. 7 generates a digital watermark characteristic parameter table. Thegenerated digital watermark characteristic parameter table is stored inthe record medium unit 153. The digital watermark characteristicparameter table stored in the record medium unit 153 is input to thedigital watermark characteristic calculating portion 151 through theinput unit 152 and stored to a storing unit of the digital watermarkcharacteristic calculating portion 151. The operation of the digitalwatermark characteristic calculating portion 151 is the same as theoperation of the digital watermark characteristic calculating portion104 shown in FIG. 4.

[Fourth Embodiment]

Next, with reference to FIG. 12, a digital watermark characteristicparameter table generating unit according to a fourth embodiment of thepresent invention will be described.

FIG. 12 is a block diagram showing the structure of the digitalwatermark characteristic parameter table generating unit according tothe fourth embodiment of the present invention. The structure of thedigital watermark characteristic parameter table generating unitaccording to the fourth embodiment is the same as that of theconventional digital watermark characteristic table generating unitshown in FIG. 2 except that a digital watermark characteristic parametertable calculating portion 205 is used instead of the digital watermarkcharacteristic table generating portion 2201. The digital watermarkcharacteristic parameter table calculating portion 205 obtainsinformation that describes an image quality deterioration ratio and adetection ratio with a detected result that is received from the digitalwatermark detecting portion 202, a digital watermark strength, a attackparameter, an image quality deterioration amount that is received fromthe image quality deterioration amount calculating portion 203, and acategory index that is received from the categorizing portion 204. Thedigital watermark characteristic parameter table calculating portion 205outputs a table that describes the relation between these factors, acategory index, and a digital watermark strength as a digital watermarkcharacteristic parameter table.

Next, the operation of the digital watermark characteristic parametertable generating unit shown in FIG. 12 will be described. The operationof the digital watermark characteristic parameter table generating unitshown in FIG. 12 is the same as that of the conventional digitalwatermark characteristic table generating unit shown in FIG. 2 exceptfor a digital watermark characteristic parameter table calculatingportion 205. Next, with reference to FIG. 13, the digital watermarkcharacteristic parameter table calculating portion 205 will be describedin detail.

FIG. 13 is a block diagram showing the structure of a digital watermarkcharacteristic parameter table calculating portion 205. A detectedresult totaling portion 300 totals detected results for each attackparameter, each digital watermark strength, and each category index,calculates detection ratios with the totaled results, and outputs thecalculated detection ratios to a digital watermark characteristicextracting portion 302. An image quality deterioration amount totalingportion 301 totals image quality deterioration amounts for each categoryindex and each digital watermark strength, calculates image qualitydeterioration ratios with the totaled results, and outputs thecalculated image quality deterioration ratios to a digital watermarkcharacteristic extracting portion 302.

The digital watermark characteristic extracting portion 302 obtains therelation of attack parameters and detection ratios that are receivedfrom the detected result totaling portion 300, calculates detectionratio descriptive information that describes a curve that approximatesthe relation, and outputs the calculated result to a data combiningportion 303. In addition, the digital watermark characteristicextracting portion 302 calculates image quality deterioration ratiodescriptive information that describes the image quality deteriorationamounts that are received from the image quality deterioration amounttotaling portion 301 and outputs the calculated result to the datacombining portion 303. The data combining portion 303 generates a tablethat describes the relation between a category index, a digitalwatermark strength, and the detection ratio descriptive information andthe image quality deterioration ratio descriptive information that arereceived from the digital watermark characteristic extracting portion302 and outputs the generated table as a digital watermarkcharacteristic parameter table.

Next, the operation of the digital watermark characteristic parametertable calculating portion shown in FIG. 13 will be described. Theoperations of the detected result totaling portion 300 and the imagequality deterioration amount totaling portion 301 of the digitalwatermark characteristic parameter table calculating portion shown inFIG. 13 are the same as the operations of the detected result totalingportion 300 and the image quality deterioration amount totaling portion301 of the conventional digital watermark characteristic parameter tablecalculating portion. When a detection ratio for a complex attack iscalculated, detection ratios of attack parameters of individual singleattacks composing the complex attack are calculated.

A detection ratio that is output from the detected result totalingportion 300 and an image quality deterioration ratio that is output fromthe image quality deterioration amount totaling portion 301 are input tothe digital watermark characteristic extracting portion 302. The digitalwatermark characteristic extracting portion 302 obtains image qualitydeterioration ratio descriptive information and detection ratiodescriptive information, and outputs to the data combining portion 303.

The image quality deterioration ratio descriptive information may be animage quality deterioration ratio or an image quality deteriorationratio curve parameter.

On the other hand, the detection ratio descriptive information may be adetection ratio characteristic parameter or a detection ratiocharacteristic general parameter. In the case of a single attack that isa single process, the detection ratio characteristic parameter is adetection ratio curve parameter. In the case of a complex attack, thedetection ratio characteristic parameter is composed of a detectionratio curve parameter and an attack correlation curved surfaceparameter. In the case of a single attack, the detection ratiocharacteristic general parameter is a detection ratio curve generalparameter. In the case of a complex attack, the detection ratiocharacteristic general parameter is composed of a detection ratio curvegeneral parameter and an attack correlation curved surface generalparameter.

The structure and operation of the digital watermark characteristicextracting portion 302 depend on whether the image quality deteriorationratio descriptive information is an image quality deterioration ratio oran image quality deterioration ratio curve parameter. In addition, thestructure and operation of the digital watermark characteristicextracting portion 302 depend on whether the detection ratio descriptiveinformation is a detection ratio characteristic parameter or a detectionratio characteristic general parameter. Moreover, the structure andoperation of the digital watermark characteristic extracting portion 302depend on whether a single attack or a complex attack is applied. Thesecases will be described later in detail.

The data combining portion 303 generates a digital watermarkcharacteristic parameter table with the image quality deteriorationratio descriptive information and the detection ratio descriptiveinformation that are received from the digital watermark characteristicextracting portion 302 and outputs the generated digital watermarkcharacteristic parameter table. The operation of the data combiningportion 303 will be described later along with the operation of thedigital watermark characteristic extracting portion 302.

Next, with reference to FIG. 14, the structure and operation of thedigital watermark characteristic extracting portion 302 shown in FIG. 13in the case that the image quality deterioration ratio descriptiveinformation is an image quality deterioration ratio and that thedetection ratio descriptive information is a detection ratiocharacteristic parameter will be described.

FIG. 14 is a schematic diagram showing an example of the structure ofthe digital watermark characteristic extracting portion 302 according tothe present invention. The detection ratio characteristic extractingportion 320 obtains the relation of a detection ratio that is receivedfrom the detected result totaling portion 300 shown in FIG. 13, anattack parameter, and a digital watermark strength for each categoryindex, calculates detection ratio characteristic parameters with theobtained results, and outputs the calculation results as detection ratiodescriptive information. On the other hand, image quality deteriorationratios are input to the digital watermark characteristic extractingportion 302 from the image quality deterioration amount totaling portion301 and outputs them as image quality deterioration ratio descriptiveinformation.

Next, the operation of the detection ratio characteristic extractingportion 320 shown in FIG. 14 will be described. First of all, theoperation of the detection ratio characteristic extracting portion 320in the case of a single attack will be described. In this case, thedetection ratio characteristic parameter is a detection ratio curveparameter.

Next, the method for calculating a detection ratio curve parameter withthe relation (x(1), r(x(1))), (x(2), r(x(2)), . . . , (x(N), r(x(N))) ofan attack parameter x at N points and a detection ratio r(x) will bedescribed in the assumption that the relation of x(1)≦x(2)≦ . . . ≦x(N)is satisfied.

First of all, the detection ratio characteristic extracting portion 320checks the variation of the detection ratio against the attack parameterand approximates the variation with a curve defined by severalparameters. The type of curve that approximates the variation depends onthe digital watermark system and attack for use. The type of curve maybepre-designated for each attack. Alternatively, the detection ratiocharacteristic extracting portion 320 may calculate a detection ratiocurve parameter for each curve that has been registered and select acurve that has the minimum approximation error.

When a logistic curve expressed by the formula (7) is used, thefollowing relation is satisfied: $\begin{matrix}{{c_{1}\left( {x + c_{2}} \right)} = {\ln \left\lbrack {\frac{1}{r(x)} - 1} \right\rbrack}} & (32)\end{matrix}$

Thus, for the N points, the detection ratio characteristic extractingportion 320 obtains the relation of the amount of the right side of theformula (32) and the attack parameter x and approximates the relationwith a line, and obtains detection ratio curve parameters c1 and c2. Toapproximate the relation with a line, for example, the method of leastsquares can be used.

For a curve that satisfies the following formula as with a logisticcurve: $\begin{matrix}{u = \frac{1}{1 + {\mathcal{e}}^{\prime}}} & (33)\end{matrix}$

when v is changed by a small amount Δv, the amount of change Δu of u isexpressed by the following formula: $\begin{matrix}{{\Delta \quad u} = {{{- \frac{{\mathcal{e}}^{\prime}}{\left( {1 + {\mathcal{e}}^{\prime}} \right)^{2}}}\Delta \quad v} = {{- {u\left( {1 - u} \right)}}\Delta \quad v}}} & (34)\end{matrix}$

Thus, the influence of the approximation error against the line isproportional to the following formula:

r(x)(1−r(x))  (35)

Thus, when coefficients are calculated by the method of least squares,approximation errors can be weighted with the value of the formula (35)or a value as a function thereof. In other words, they are weightedaccording to the following formula: $\begin{matrix}{\sum\limits_{x - 1}^{N}{{r\left( {x(n)} \right)}\left\{ {1 - {r\left( {x(n)} \right)}} \right\} \left\{ {{c_{1}\left( {{x(n)} + c_{2}} \right)} - {\ln \left( {\frac{1}{r\left( {x(n)} \right)} - 1} \right)}} \right\}^{2}}} & (36)\end{matrix}$

Parameters that minimize the coefficients are calculated. Thus, thetotal approximation error can be suppressed.

When a graph of broken lines expressed by the formula (8) is used, apoint that satisfies the following relation of the detection ratio isselected from the N points and the selected point is directly applied,the detection ratio curve parameters c1 and c2 can be obtained:

r1≦r(x)≦r ₂  (37)

where r1 and r2 may be any values as long as they satisfy the followingrelation:

0≦r₁<r₂≦1  (38)

For example, r1=0.1 and r2=0.9.

When a graph of broken lines expressed by the formula (9) is used, the Npoints are separated into n points close to 0 and the remaining (N−n)points. Lines comprising the two portions are obtained. With the pointof intersection of these lines and the point of intersection of the lineof the (N−n) points and the axis of the attack parameter, the detectionratio curve parameters c1, c2, and c3 can be obtained. In this case, npoints can be selected in various manners. For example, an integer thatsatisfies the relation of 1≦n≦N that allows the approximation error tobe minimum can be used.

When a fractional function expressed by the formula (10) is used as acurve, the following relation is satisfied: $\begin{matrix}{{{c_{1}x} + {c_{2}x^{2}}} = {\frac{1}{r(x)} - 1}} & (39)\end{matrix}$

Thus, for the N points, the relation of the amount of the right side ofthe formula (39) and the attack parameter x is obtained and applied to aquadratic function. Consequently, the detection ratio curve parametersc1 and c2 can be obtained. To applies the relation to a quadraticfunction, for example, the method of least squares can be used.

For a curve that satisfies the following formula: $\begin{matrix}{u = \frac{1}{v}} & (40)\end{matrix}$

when v is changed by a small amount Δv, the amount of change Δu of u canbe expressed by the following formula: $\begin{matrix}{{\Delta \quad u} = {{{- \frac{1}{v^{2}}}\Delta \quad v} = {{- u^{2}}\quad \Delta \quad v}}} & (41)\end{matrix}$

Thus, the method of least squares can be used along with the weightingmethod. When an exponential function expressed by the formula (11) isused as a curve, the following relation is satisfied:

−c ₁(x−c ₂)=lnr(x)  (42)

Thus, for the N points, the relation of the natural logarithm of thedetection ratio and the attack parameter x is obtained and applied to aline. Thus, the detection ratio curve parameters c1 and c2 are obtained.As a method for applying the relation to a line, for example, the methodof least squares can be used.

For a curve that is expressed by the following formula:

u=e^(−v)  (43)

when v is changed by a small amount Δv, the amount of change Δu of u isexpressed by the following formula:

Δu=−e ^(−v) Δv=−uΔv  (44)

The method of least squares maybe used along with the weighting method.In such a manner, detection ratio curve parameters are calculated foreach category index and each digital watermark strength. Along with theimage quality deterioration ratio descriptive information, the detectionratio curve parameters are output as detection ratio descriptiveinformation to the data combining portion 303. The data combiningportion 303 generates and outputs a digital watermark characteristicparameter table as shown in Table 2 for each category index.

Next, the operation of the digital watermark characteristic calculatingportion shown in FIG. 14 in the case of a complex attack will bedescribed. In this case, the detection ratio characteristic parameter iscomposed of a detection ratio curve parameter of each single attackcomposing the complex attack and an attack correlation curved surfaceparameter.

In the above-described manner, a detection ratio curve parameter foreach single attack composing a complex attack is calculated.

Next, a method for calculating an attack correlation curved surfaceparameter with a detection ratio r1,2(x1, x2) at N points (x1(1),x2(1)), (x1(2) x2(2)), . . . , (x1(N), x2(N)) will be described.

First of all, for N combinations of (x1, x2), an attack correlationvalue z1,2(x1, x2) expressed by the formula (16) is calculated withvalues r1(x1) and r2(x2) obtained from a detection ratio curve for asingle attack and a detection ratio r1,2(x1, x2) of a complex attack.The N pieces of data of (x1, x2, z1,2(x1, x2)) are applied to a curvedsurface. When an attack correlation value is calculated, really measureddetection ratios for single attacks can be used instead of valuesobtained from the detection ratio curve. When a function expressed bythe formula (20) is used as a curved surface, the following relation issatisfied: $\begin{matrix}{{{\ln \quad q_{1}} + {q_{2}\ln \quad x_{1}} + {q_{3}\ln \quad x_{2}}} = {\ln \left\lbrack {\frac{1}{z_{1,2}\left( {x_{1},x_{2}} \right)} - 1} \right\rbrack}} & (45)\end{matrix}$

Thus, when the relation of the logarithmic values of attack parametersand the amount of the right side of the formula (45) is obtained for theN points and applied to a plane, ln q1, q2, and q3 can be obtained.Thus, the value of q1 can be obtained with ln q1. Consequently, attackcorrelation curved surface parameters can be calculated. When therelation is applied to a plane, for example, the method of least squarescan be used.

In this case, the formula (20) can be expressed by the followingformula: $\begin{matrix}{{z_{1,2}\left( {x_{1},x_{2}} \right)} = \frac{1}{1 + {\exp \left( {{\ln \quad q_{1}} + {q_{2}\ln \quad x_{1}} + {q_{3}\ln \quad x_{2}}} \right)}}} & (46)\end{matrix}$

In addition, for a curve that satisfies the formula (33), when v ischanged by a small amount Δv, the amount of change Δu of u is expressedby the formula (34). Thus, as with the formula (36), the method of leastsquares can be used along with the weighting method. When single attackdetection ratios r1(x1) and r2(x2) are small, the formula (23) showsthat the influence of the approximation error of z1,2(x1, x2) againstthe complex attack detection ratio r1,2(x1, x2) is small. Thus, accurateapproximation of z1,2(x1, x2) is required only in the range of which thesingle attack detection ratios r1(x1) and r2(x2) are large.

When a function expressed by the formula (21) is used as a curvedsurface, it is difficult to obtain parameters that analytically minimizethe approximation error. However, when a proper algorithm such as thesteepest descent method is used, attack correlation parameters can becalculated. In such a manner, attack correlation curve surfaceparameters for each category index and each digital watermark strengthare calculated. In addition to the image quality deterioration ratiodescriptive information, the calculated attack correlation curvedsurface parameters and the single attack detection ratio curveparameters are output as the detection ratio descriptive information tothe data combining portion 303. The data combining portion 303 generatesand outputs digital watermark characteristic parameter tables shown inTables 2 and Tables 3 for individual category indexes.

Next, with reference to FIG. 15, the structure and operation of thedigital watermark characteristic extracting portion 302 shown in FIG. 13in the case that the image quality deterioration ratio descriptiveinformation is an image quality deterioration ratio curve parameter andthat the detection ratio descriptive information is a detection ratiocharacteristic general parameter will be described. FIG. 15 is a blockdiagram showing an example of the structure of the digital watermarkcharacteristic extracting portion 302 according to the presentinvention.

For each category index, a detection ratio characteristic parametercalculating portion 340 obtains the relation of a detection ratio thatis received from the detected result totaling portion 300 shown in FIG.13, an attack parameter, and a digital watermark strength, calculatesdetection ratio characteristic general parameters with the obtainedrelation, and outputs the calculated detection ratio characteristicgeneral parameters as detection ratio descriptive information. An imagequality deterioration ratio characteristic extracting portion 341obtains the relation of an image quality deterioration ratio that isreceived from the image quality deterioration amount totaling portion301 shown in FIG. 13 and a digital watermark strength, calculates imagequality deterioration ratio curve parameters with the obtained relation,and outputs the calculated image quality deterioration ratio curveparameters as image quality deterioration ratio descriptive information.

Next, the operation of the digital watermark characteristic extractingportion shown in FIG. 15 will be described.

First of all, the operation of the digital watermark characteristicextracting portion in the case of a single attack will be described. Inthis case, the detection ratio characteristic general parameter is adetection ratio curve general parameter. The image quality deteriorationratio characteristic extraction portion 341 calculates parameters of animage quality detection ratio curve that approximates the variation ofan image quality deterioration ratio against a digital watermarkstrength. For example, the image quality detection ratio characteristicextracting portion 341 approximates the variation with a quadraticfunction expressed by the formula (29), by fitting a quadratic curve tothe variation, image quality detection ratio curve parameters b1, b2,and b3 can be calculated. In this case, for example, the method of leastsquares can be used. The obtained image quality deterioration ratiocurve parameters are output as image quality deterioration ratiodescriptive information to the data combining portion 303 shown in FIG.13.

The detection ratio characteristic extracting portion 340 calculatesdetection ratio characteristic general parameters. In the same manner asthe digital watermark characteristic extracting portion 320 shown inFIG. 14, the detection ratio characteristic extracting portion 340calculates detection ratio curve parameters for each category index andeach digital watermark strength. Thereafter, the detection ratiocharacteristic extracting portion 340 obtains the variation of detectionratio curve parameters against each digital watermark strength, fits acurve to the variation, and calculates detection ratio curve generalparameters.

When the variation of detection ratio curve parameters against eachdigital watermark strength is approximated with a quadratic functionexpressed by the formula (30), by fitting a quadratic curve to thevariation, detection ratio curve general parameters p1, p2, and p3 canbe calculated. In this case, for example, the method of least squarescan be used. The obtained detection ratio curve general parameters areoutput as detection ratio descriptive information to the data combiningportion 303 shown in FIG. 13. The data combining portion 303 generatesand outputs a digital watermark characteristic parameter table as shownin Table 4.

Next, the operation of the digital watermark characteristic extractingportion shown in FIG. 15 in the case of a complex attack will bedescribed. In this case, the detection ratio characteristic generalparameter is composed of a detection ratio curve general parameter foreach attack composing a complex attack and a correlation curved surfacegeneral parameter.

The operation of the image quality deterioration ratio characteristicextracting portion 341 in the case of a complex attack is the same asthat in the case of a single attack. The image quality deteriorationratio characteristic extracting portion 341 outputs image qualitydeterioration ratio curve parameters as image quality deteriorationratio descriptive information to the data combining portion 303 shown inFIG. 13.

For each attack composing the complex attack, the detection ratiocharacteristic extracting portion 340 calculates detection ratio curveparameters and detection ratio curve general parameters in the samemanner as the digital watermark characteristic extracting portion 342performs for a single attack. In addition, the detection ratiocharacteristic extracting portion 340 calculates attack correlationcurved surface parameters in the same manner as the digital watermarkcharacteristic extracting portion 320 shown in FIG. 14 for a complexattack. The detection ratio characteristic extracting portion 340obtains the variation of attack correlation curved surface parametersagainst each digital watermark strength and calculates parameters of acurve that approximates the variation.

For example, when the variation of attack correlation curved surfaceparameters against each digital watermark strength is approximated witha quadratic function expressed by the formula (31), by fitting aquadratic curve to the variation, attack correlation curved surfacegeneral parameters t1, t2, and t3 can be calculated. The obtaineddetection ratio curve general parameters and attack correlation curvedsurface general parameters are output as detection ratio descriptiveinformation to the data combining portion 303 shown in FIG. 13. The datacombining portion 303 generates and outputs digital watermarkcharacteristic parameter tables shown in Tables 4 and 5.

The digital watermark inserting system and the digital watermarkcharacteristic parameter table generating unit have been described.Next, a record medium according to the present invention will bedescribed. On the record medium, a program that allows the digitalwatermark inserting system and the digital watermark characteristicparameter table generating unit to be accomplished has been recorded.

The program for the digital watermark inserting system and the digitalwatermark characteristic parameter table generating unit is coded in aprogram language of which a computer reads the program. The recordmedium is for example a CD-ROM or a floppy disk.

The record medium may be a record means such as a hard disk of a serverunit. When the computer program is recorded to the storing means andread through a network, the record medium according to the presentinvention can be accomplished.

EXAMPLES First Example

Next, examples of the embodiments of the present invention will bedescribed.

FIG. 16A is an example of a graph showing the variation of a detectionratio against an attack for adding noise. As an attack parameter, thestandard deviation of noise was used. The digital watermark strength wasvaried in the range from 1 to 4 (1, 2, 3, and 4). The approximatedresults of the variation with a logistic curve expressed by the formula(7), with a graph of broken lines expressed by the formula (8), and witha graph of broken lines expressed by the formula (9) are shown in FIGS.16B, 16C, and 16D, respectively. The respective digital watermarkcharacteristic parameter tables are shown in Tables 6, 7, and 8.

TABLE 6 Image quality Detection ratio Digital watermark deteriorationcurve parameters strength ratio (c1, c2) 1 0.693 0.6408, −10.46 2 0.6440.3556, −14.61 3 0.533 0.3207, −18.36 4 0.347 0.1422, −30.27

TABLE 7 Image quality Detection ratio Digital watermark deteriorationcurve parameters strength ratio (c1, c2) 1 0.693  5.59, 15.38 2 0.644 6.36, 23.35 3 0.533  9.08, 26.93 4 0.347 10.89, 52.91

TABLE 8 Image quality Detection ratio Digital watermark deteriorationcurve parameters strength ratio (c1, c2, c3) 1 0.693  5.98, 0.9601,15.38 2 0.644  6.56, 0.9158, 23.30 3 0.533 11.95, 0.9467, 25.37 4 0.34716.78, 0.8779, 50.92

Image quality deterioration ratios D in the tables are calculated withSNR values according to the formula (47): $\begin{matrix}{D = \left\{ \begin{matrix}1 & \left( {{SNR} > 45} \right) \\{\left( {{SNR} - 30} \right)/15} & \left( {30 \leq {SNR} \leq 45} \right) \\0 & \left( {{SNR} < 30} \right)\end{matrix} \right.} & (47)\end{matrix}$

FIG. 18A is a graph showing the variation of a detection ratio againstthe enlargement/shrinkage in the horizontal direction as another attackexample. As an attack parameter, the magnification of theenlargement/shrinkage was used. The digital watermark strength wasvaried in the range from 1 to 4 (1, 2, 3, and 4).

The following curves were fitted to the variation. In this case, whenthe attack parameter x is 1, it represents that there is no attack.Thus, in two cases x<1 and x>1 respectively, respective curves werefitted to the variation. In this case, each parameter is shown in FIGS.17A, 17B, and 17C. The approximated results of the variation with alogistic curve expressed by the formula (7), with a graph of brokenlines expressed by the formula (8), and with a graph of broken linesexpressed by the formula (9) are shown in FIGS. 18B, 18C, and 18D,respectively. The respective digital watermark characteristic parametertables are shown in Tables 9, 10, and 11, respectively.

TABLE 9 Image quality Detection ratio Digital watermark deteriorationcurve parameters strength ratio (c1, c2, c3, c4) 1 0.693 −142.6, 136.6,124.3, −130.4 2 0.644 −124.8, 119.0, 122.9, −129.6 3 0.533 −171.8,163.6, 129.7, −137.6 4 0.347 −131.6, 124.9, 106.0, −112.9

TABLE 10 Image quality Detection ratio Digital watermark deteriorationcurve parameters strength ratio (c1, c2, c3, c4) 1 0.693 0.942, 0.974,1.031, 1.071 2 0.644 0.933, 0.974, 1.034, 1.075 3 0.533 0.933, 0.966,1.042, 1.082 4 0.347 0.928, 0.969, 1.043, 1.093

TABLE 11 Digital Image quality Detection ratio curve watermarkdeterioration parameters strength ratio (c1, c2, c3, c4, c5, c6) 1 0.6930.941, 0.974, 0.989, 1.034, 0.918, 1.072 2 0.644 0.933, 0.973, 0.988,1.036, 0.956, 1.075 3 0.533 0.937, 0.963, 0.955, 1.044, 0.943, 1.082 40.347 0.928, 0.967, 0.948, 1.046, 0.935, 1.093

Thus, in such a manner, detection ratio curve parameters can becalculated.

FIG. 19A shows an example of which the variation of an image qualitydeterioration ratio expressed by the formula (47) against a digitalwatermark strength was checked and an image quality deterioration ratiocurve was obtained. In FIG. 19A, the image quality deterioration ratiowas approximated with a quadratic function. FIG. 19B shows an example ofwhich an image quality deterioration curve was obtained in anotherdigital watermark system. In FIG. 19B, the digital watermark strengthwas approximated with a graph of broken lines. In such a manner, byfitting a curve to the variation, image quality deterioration ratiocurve parameters can be calculated. Next, an example of which thevariation of a detection ratio curve parameter against a digitalwatermark strength was checked and approximated with a quadratic curveis described.

FIGS. 20A and 20B are examples of which quadratic functions are fittedto detection ratio curve parameters c1 and c2 shown in Table 6,respectively. FIGS. 21A and 21B are examples of which quadraticfunctions are fitted to detection ratio curve parameters c1 and c2 shownin Table 7, respectively. FIGS. 22A, 22B, and 22C are examples of whichquadratic functions are fitted to detection ratio curve parameters c1,c2, and c3 shown in Table 8, respectively. In such a manner, by fittinga curve to the variation, detection ratio curve general parameters canbe obtained.

Next, an example of which an attack correlation curved surface iscalculated for a complex attack is described. FIG. 23 shows a calculatedresult of an attack correlation value against a complex attack that is acombination of a chromatic saturation varying attack and a noise addingattack according to the formula (16). For the variation of chromaticsaturation, the ratio of varied chromatic saturation against originalchromatic saturation is used as an attack parameter. FIG. 23 shows thatthe complex attack does not have a synergism effect of a combination ofsingle attacks.

When a weighting function separable for each variable is used, arobustness evaluation value against a complex attack of the variation ofchromatic saturation and noise can be expressed by a produce of arobustness evaluation value against the variation of chromaticsaturation and a robustness evaluation value against noise. Thus, thecalculation amount can be reduced. When the robustness evaluation valueis approximated with the following formula (48), in the region of whichthe standard deviation of noise is large, the accuracy of approximationis low. In the region, since the detection ratio against noise is small,the influence of an error against a calculated robustness evaluationvalue is small.

Z _(1,2)(X ₁ ,X ₂)=1  (48)

Next, an example of which an attack correlation curved surface againstanother complex attack will be described. FIG. 24A shows a calculatedresult of an attack correlation value for a complex attack of acombination of an image cropping attack and a noise adding attackaccording to Formula (16). For the image slicing attack, the area ratioof the cropped image against the original image is used as an attackparameter.

On the other hand, for the noise adding attack, standard deviation isused as an attack parameter. FIG. 24B shows an example of an attackcorrelation curved surface approximated with a curved surface expressedby the formula (20). However, for the image cropping attack, when theattack parameter value x is 1, it represents that there is no attack.Thus, the curve expressed by the formula (20) was horizontally moved andinverted. In such a manner, a curve surface can be fitted to thevariation and an attack correlation curved surface parameter can beobtained.

For a complex attack of a combination of an image cropping attack and anoise adding attack, by varying the digital watermark strength, anattack correlation curved surface parameter is calculated. The variationis approximated with a quadratic function. The results are shown inFIGS. 25A, 25B, and 25C. Thus, in such a manner, by fitting a curve tothe variation, attack correlation curved surface general parameters canbe calculated.

According to the present invention, since data for obtaining a detectionratio is stored rather than a robustness evaluation value, the user cancustomize a method for calculating a digital watermark robustnessevaluation value before inserting a digital watermark into an image.

In addition, since detection ratio characteristic and image qualitydeterioration characteristic are stored as parameters that approximatedetection ratio data and image quality deterioration data, the dataamount to be stored can be remarkably reduced in comparison with thecase that detection ratio data is stored. Thus, the memory amount can bereduced.

Moreover, since detection ratio data for each single attack thatcomposes a complex attack and data that has a synergism effect ofattacks are separately stored, robustness evaluation values for singleattacks and a complex attack can be effectively calculated. In addition,for complicated and plurality of types of digital watermark information,robustness evaluation value, image quality deterioration ratio, anddigital watermark strength can be analyzed. Although the presentinvention has been shown and described with respect to a best modeembodiment thereof, it should be understood by those skilled in the artthat the foregoing and various other changes, omissions, and additionsin the form and detail thereof may be made therein without departingfrom the spirit and scope of the present invention.

What is claimed is:
 1. A digital watermark characteristic parametertable generating method for inserting digital watermark information intoan input image, comprising the steps of: calculating a feature amount ofthe input image, categorizing the input image based on the calculatedresult, and outputting a category index as the categorized result;converting input embedding information into the digital watermarkinformation, inserting the digital watermark information into the inputimage with input digital watermark strength, and generating a digitalwatermark inserted image as the inserted data; adjusting the strength ofan attack with an input attack parameter, attacking the digitalwatermark inserted image with the adjusted attack strength, generating aresultant attacked image, detecting a digital watermark from theattacked image, outputting the detected result; comparing the inputimage with the digital watermark inserted image, calculating an imagequality deterioration amount caused by the inserted digital watermark,and outputting the calculated image quality deterioration amount; andreceiving the detected result of the digital watermark, the digitalwatermark strength, the attack parameter, the image qualitydeterioration amount, and the category index, totaling the detectedresults for each of combinations of the category index, the digitalwatermark strength, and the attack parameter, obtaining a detectionratio as the totaled result, totaling the image quality deteriorationamount for each of combinations of the category index and the digitalwatermark strength, obtaining an image quality deterioration ratio asthe totaled result, and calculating a digital watermark characteristicparameter table by using the detection ratio and the image qualitydeterioration ratio, and outputting the digital watermark characteristicparameter table.
 2. A record medium from which a computer reads aprogram of a digital watermark characteristic parameter table generatingmethod used for inserting digital watermark information into an inputimage, the program causing the computer to perform the method, themethod comprising the steps of: calculating a feature amount of theinput image, categorizing the input image with the calculated result,and outputting a category index as the categorized result; convertinginput embedding information into the digital watermark information,inserting the digital watermark information into the input image withinput digital watermark strength, and generating a digital watermarkinserted image as the inserted data; adjusting the strength of an attackwith an input attack parameter, attacking the digital watermark insertedimage with the adjusted attack strength, generating a resultant attackedimage, detecting a digital watermark from the attacked image, outputtingthe detected result; comparing the input image with the digitalwatermark inserted image, calculating an image quality deteriorationamount caused by the inserted digital watermark, and outputting thecalculated the image quality deterioration amount; and receiving thedetected result of the digital watermark, the digital watermarkstrength, the attack parameter, the image quality deterioration amount,and the category index, totaling the detected results for each ofcombinations of the category index, the digital watermark strength, andthe attack parameter, obtaining a detection ratio as the totaled result,totaling the image quality deterioration amount for each of combinationsof the category index and the digital watermark strength, obtaining animage quality deterioration ratio as the totaled result, and calculatinga digital watermark characteristic parameter table by using thedetection ratio and the image quality deterioration ratio, andoutputting the digital watermark characteristic parameter table.